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Cite this article: Billingsley J, Gomes CM,

McCullough ME. 2018 Implicit and explicit

influences of religious cognition on Dictator Game

transfers. R. Soc. open sci. 5: 170238.

http://dx.doi.org/10.1098/rsos.170238

Received: 13 March 2018

Accepted: 26 July 2018

Subject Category:Psychology and cognitive neuroscience

Subject Areas:psychology/cognition/behaviour

Keywords:religious priming, Dictator Game, religion,

cooperation, prosocial

Author for correspondence:Joseph Billingsley

e-mail: [email protected]

& 2018 The Authors. Published by the Royal Society under the terms of the CreativeCommons Attribution License http://creativecommons.org/licenses/by/4.0/, which permitsunrestricted use, provided the original author and source are credited.

Electronic supplementary material is available

online at https://doi.org/10.6084/m9.figshare.c.

4190015.

Implicit and explicit influencesof religious cognition onDictator Game transfersJoseph Billingsley, Cristina M. Gomes

and Michael E. McCullough

Department of Psychology, University of Miami, Coral Gables, FL, USA

JB, 0000-0002-6577-175X

Does religion promote prosocial behaviour? Despite numerous

publications that seem to answer this question affirmatively,

divergent results from recent meta-analyses and pre-registered

replication efforts suggest that the issue is not yet settled.

Uncertainty lingers around (i) whether the effects of religious

cognition on prosocial behaviour were obtained through

implicit cognitive processes, explicit cognitive processes or

both and (ii) whether religious cognition increases generosity

only among people disinclined to share with anonymous

strangers. Here, we report two experiments designed to

address these concerns. In Experiment 1, we sought to replicate

Shariff and Norenzayan’s demonstration of the effects of

implicit religious priming on Dictator Game transfers to

anonymous strangers; unlike Shariff and Norenzayan,

however, we used an online environment where anonymity

was virtually assured. In Experiment 2, we introduced a

‘taking’ option to allow greater expression of baseline

selfishness. In both experiments, we sought to activate

religious cognition implicitly and explicitly, and we

investigated the possibility that religious priming depends on

the extent to which subjects view God as a punishing,

authoritarian figure. Results indicated that in both

experiments, religious subjects transferred more money on

average than did non-religious subjects. Bayesian analyses

supported the null hypothesis that implicit religious priming

did not increase Dictator Game transfers in either experiment,

even among religious subjects. Collectively, the two

experiments furnished support for a small but reliable effect of

explicit priming, though among religious subjects only. Neither

experiment supported the hypothesis that the effect of

religious priming depends on viewing God as a punishing

figure. Finally, in a meta-analysis of relevant studies, we

found that the overall effect of implicit religious priming on

Dictator Game transfers was small and did not statistically

differ from zero.

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https://doi.org/10.6084/m9.figshare.c.4190015

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sci.5:1702382


1. IntroductionProsocial encouragement features prominently in the world’s major religions [1]. These religions urge

adherents to love neighbours or even strangers as they would themselves (e.g. Leviticus 19:18;

Leviticus 19:34; Mark 12:31), to provide charity to those in need (e.g. Surah Al-Baqarah 2:83; Mark

10:17–23) and to act toward others as they would have others act toward them (e.g. Matthew 7:12;

Talmud Shabbat 31a).

This association of world religions with benevolence toward others has motivated, in part, the

development of at least two related bodies of theory. First is the ‘religious prosociality hypothesis’

[2, p. 876], defined variously as the notion that ‘religions facilitate costly behaviors that benefit other

people’ [3, p. 58] or that ‘religious belief or concepts lead to prosocial attitudes and behaviors’

[2, p. 876]. Second is a broader set of claims fashioned into a cultural evolutionary model of religious

prosociality [4,5] (for related proposals, see also [6–9]). According to this model, cultures have varied

in the extent to which deities and other supernatural agents are understood to involve themselves in

human affairs and care about prosociality or other moralized behaviours. Past cultures marked by

supernatural agents of relatively greater power and heightened interest in moralizing human

behaviour—‘Big Gods’ for short [4]—would have experienced higher levels of prosocial behaviour

compared to cultures with weak, morally indifferent deities. In turn, the increased levels of

prosociality promoted by powerful moralizing deities would have facilitated the emergence of large-

scale societies where cooperation occurs even within groups of individuals who are not closely related

by recent ancestry and who do not regularly interact [5]. In addition, increased levels of prosociality

would have fostered greater social solidarity and ultimately greater success in competition with other

cultures [5]. If true, this model would thus help account simultaneously for the rise of large-scale

cooperative societies, and for the prevalence of prosocial norms across the contemporary religious

landscape [5].

An impressive array of research has been brought to bear on these two inter-related bodies of theory.

In the case of the religious prosociality hypothesis, much work has focused on its prediction that religious

individuals should behave more prosocially than the non-religious. Self-report measures reliably indicate

that religious individuals do indeed profess higher levels of various prosocial behaviours than non-

religious individuals, including such behaviours as volunteering, charitable giving, sharing and

generosity [1–3,10]. But, studies also indicate that religiosity is positively associated with social

desirability, suggesting that religious individuals may simply report greater prosocial behaviours

because they are more sensitive to being perceived as other-oriented [11]—without, in fact, acting

more prosocially. Research eschewing self-report in favour of observable behavioural outcomes

generally indicates a nuanced relationship. While some behavioural studies have reported no effect of

religiosity, most reviews of the behavioural literature suggest that there is indeed an association of

religion with prosociality, but that it is tightly circumscribed by multiple factors [1,2]. These factors

include the target’s need state [1], the target’s overall social distance from the participant [1] and

whether the target is an ingroup or an outgroup member [2,3].

The self-report and behavioural studies reviewed above have been offered as support for the cultural

evolutionary model, as well as for the religious prosociality hypothesis. But, in the case of the cultural

evolutionary model, researchers have also turned to anthropology, archaeology and history for

corroborating evidence. Consistent with the cultural evolutionary model, their analyses of

ethnographic accounts and the historical record indicate that religion in traditional, small-scale

societies is generally characterized by supernatural agents with little interest in human moral affairs,

and little ability to influence prosocial behaviour. As societies increase in scale, however, religion has

become marked by powerful moralizing agents that actively monitor human affairs and administer

rewards and punishments for behaviours regulated by prosocial norms (for review, see [3,5]).

Self-report, behavioural observations and findings from ethnography and history thus offer support

for both the religious prosociality hypothesis and the cultural evolutionary model. But, both theories

include a crucial claim of causation—that religion (at least when marked by moralizing supernatural

agents) actively increases prosocial behaviour. Any causal claim is best supported by experimental

evidence; therefore, findings from the experimental literature are particularly important in evaluating

these dual bodies of theory. In the case of the religious prosociality hypothesis, correlations of

religiosity with either self-reported or observed prosociality might reflect a tendency of more

prosocially oriented individuals to adopt religious beliefs and practices, rather than indicating a causal

role of religion [1]. Alternatively, a third variable might account for the association—for instance,



sci.5:1702383


individuals with a more agreeable personality might tend to be both more prosocial and more religious

[1]. In the case of the cultural evolutionary model, conclusions that religious beliefs and practices

characterized by Big Gods co-occurred with the emergence of large-scale cooperative societies do not

establish that religion is causative [5]. Thus, experimental findings are a vital supplement to the

evidence provided by anthropology, history and sociology. Experimental evidence that religion can,

and does, promote prosocial behaviour bolsters the cultural evolutionary model and argues against

alternative scenarios—particularly the suggestion that the rise of prosocial religions is purely a

by-product of societal scale [5].

Experimental evidence that religion increases prosocial behaviour derives largely from studies based

on priming [2,12]. In religious priming studies, researchers present a stimulus designed to activate

religious cognition, which then (by hypothesis) influences thinking and behaviour in other domains,

without participants being consciously aware of the link between stimulus and subsequent behaviour

[12]. Multiple methods have been used to prime religion [12]. Subliminal priming [12,13] and implicit

priming techniques [14] are designed to minimize conscious awareness of the stimulus. A common

implicit priming technique, for example, is to present participants with themed target words

embedded in a scrambled sentence task (e.g. [15]). The themed words are presumed to activate the

relevant concept (e.g. religion) without participants being overtly aware of it. Explicit techniques, on

the other hand, present relevant stimuli in the laboratory without pretence of limiting conscious

awareness of the stimulus itself, but in doing so they also increase the likelihood that participants will

link awareness of the stimulus to subsequent behaviours of interest to researchers [12] and, in the

process, create experimental demand effects [16]. Contextual primes, finally, present primes in natural

field settings outside the laboratory; though the stimuli are available to conscious awareness, their

presentation may nonetheless be subtle and covert, potentially minimizing demand concerns [12].

The past decade has given rise to a substantial body of work on the behavioural effects of religious

priming, including religious priming of prosocial behaviour. Shariff et al. [12] recently meta-analysed the

full array of religious priming studies as well as religious priming studies devoted to prosociality. Of the

92 experiments they examined, 25 specifically evaluated the effect of religious priming on some measure

of prosocial behaviour. For those 25 experiments, Shariff et al.’s results indicated that religious priming

produced an average effect of g ¼ 0.27, 95% CI [0.15, 0.40]. A trim and fill analysis designed to correct

this estimate for publication bias [17] reduced the effect size estimate to g ¼ 0.18, 95% CI [0.04, 0.32].

An additional meta-analytical tool for identifying publication bias, called the p-curve technique [18],

likewise suggested the presence of a real effect even after correction for publication bias. Shariff et al.[12] also found that religious priming appeared not to affect the behaviour of non-religious

participants, contrary to the conclusions of an earlier review [2].

Shariff et al.’s meta-analytical findings [12] are important and timely, and have stimulated additional

meta-analytical inquiry. van Elk et al. [19], for example, meta-analysed Shariff et al.’s 2016 data with two

other methods that approach the problem of publication bias in different ways. The Bayesian bias

correction method [20] produced results that largely accorded with Shariff et al.’s original conclusions.

Results obtained using the PET-PEESE method [21,22], however, suggested that the population effect

size for religious priming did not statistically differ from zero. In the light of these divergent results,

van Elk et al. [19] argued that large-scale pre-registered replications of influential studies would be

necessary to resolve the discrepancies.

One particularly influential study, as van Elk et al. also noted [19], is the set of two implicit religious

priming experiments conducted by Shariff & Norenzayan [23]. Along with experiments conducted by

Pichon et al. [13] and Randolph-Seng & Nielsen [24], Shariff and Norenzayan’s two experiments are

among the earliest examples of religious priming. With 1027 citations as of July 2018 (according to

Google Scholar), Shariff and Norenzayan’s 2007 paper is by far the most cited work among studies of

religious priming and prosociality (versus 296 citations for Pichon et al. and 269 for Randolph-Seng

and Nielsen, respectively; the median number of citations is 49). Moreover, the implicit priming

technique adopted by Shariff & Norenzayan [23]—target words embedded in a scrambled sentence

task—is the most commonly employed experimental design in the study of religious prosociality [2].

Because of their considerable influence upon the experimental study of religious prosociality, Shariff

and Norenzayan’s two studies [23] formed the basis of the current experiments, and we elaborate now

upon their methods and results in some detail. In their first experiment, Shariff and Norenzayan

presented half of their subjects—those in the religious prime condition—with a set of 10 scrambled

sentences, each consisting of five words. To make sense of each set of five words, participants had to

disregard one word and rearrange the remaining four words into a meaningful sentence. Half of the

scrambled sentences contained a target word intended to prime religious cognition; the other five



sci.5:1702384


sentences contained only non-religious words. The second half of the participants were assigned to a

control condition in which there was no scrambled sentence task. All participants then took part in a

Dictator Game (DG) in the role of giver [25]. Researchers provided subjects with 10 $1 coins and

invited them to keep as many coins as they wanted for themselves and to leave as many as they

wished for another anonymous player. In a second experiment, Shariff & Norenzayan [23] added two

new conditions to the religious prime condition from their first experiment: (i) a neutral prime

condition in which participants completed scrambled sentences designed not to evoke any particular

concept and (ii) a secular prime condition in which participants completed scrambled sentences

containing target words associated with secular moral authority. Participants then completed a DG.

Both of Shariff and Norenzayan’s experiments supported the hypothesis that implicit religious

priming increases transfers in the DG [23]. In the first experiment, subjects in the religious prime

condition gave significantly more money ($4.22 on average) than did unprimed subjects ($1.84 on

average). Experiment 2 replicated those results, revealing that subjects in both the religious prime

condition and the secular prime condition allocated more money to the other player than did

participants in the neutral prime condition. In addition, the researchers found no evidence that the

religious priming effect was mediated by conscious awareness of the religious words.

In the wake of recent research questioning the replicability of much psychological research in general

[26] and the efficacy of priming studies in particular [27], Gomes & McCullough [28] attempted a direct

pre-registered replication of Shariff and Norenzayan’s two experiments, using 650 subjects. Gomes &

McCullough [28] found no significant difference in DG transfers between subjects in the neutral

condition (M ¼ $4.49; s.d. ¼ 3.49) and those in the standard religious prime condition (M ¼ $4.28;

s.d. ¼ 3.67). This failure to replicate Shariff and Norenzayan’s 2007 findings led Gomes and

McCullough to undertake a meta-analysis of all known studies examining the effect of religious

priming on DG transfers. The random-effects meta-analysis of these six experiments implied that the

overall effect of religious priming on DG transfers did not statistically differ from zero, although it

was in the positive direction, with a medium effect size, g ¼ 0.37, s.e. ¼ 0.18, p ¼ 0.09, 95% CI [20.09,

0.83]. The PET-PEESE method suggested a bias-corrected estimate of g ¼ 20.12, p ¼ 0.37, also with a

wide 95% CI [20.45 to 0.21]. The wide confidence intervals associated with both the random-effects

estimate and the PET-PEESE estimate, not to mention the small number of experiments that to that

date had directly examined the effects of religious priming on generosity in the DG, clearly indicated

that additional pre-registered replications are needed, as both Gomes & McCullough [28] and van Elk

et al. [19] argued.

Shariff & Norenzayan [29] proposed another reason for additional experiments on this topic: Gomes

and McCullough’s control subjects transferred more of their DG endowments on average (44.9%) than

did the subjects in the previous five experiments (14–33% of the endowment). The reasons for this

high baseline transfer relative to prior experiments remain unclear, but may include differences

between the populations sampled, varying levels of perceived participant anonymity or other

differences in methodology [29]. Shariff and Norenzayan suggested that the relatively high baseline

levels of generosity among Gomes and McCullough’s subjects, whatever their causes, could have

attenuated the effect of religious priming. Indeed, in their view, the relatively high levels of baseline

generosity indicated that participants in Gomes and McCullough’s experiment were already strongly

motivated toward generosity. According to Shariff & Norenzayan [29], these relatively high baseline

levels of generosity precluded a fair test of their central hypothesis that religious priming

downregulates selfishness, and suggest that Gomes and McCullough’s experiment addressed the

separate question of whether religious priming produces ‘hyperfair’ [29, p. e105] behaviour when

prosocial motivation is already high. Future research, they suggested, might fruitfully seek to address

both of these questions.

Influential theories—the religious prosociality hypothesis and a cultural evolutionary model of

religious prosociality—thus rely importantly, though not exclusively, on experimental evidence largely

derived from priming studies. But, divergent meta-analytical findings and disputed interpretations of

a failed pre-registered replication attempt leave the priming results open to ongoing scepticism and

highlight the need for additional pre-registered replication studies to help clarify the status of the

experimental evidence.

1.1. The present researchThe present research attempted to help address these issues, using two pre-registered replications of

Shariff and Norenzayan’s 2007 experiments [23]. These pre-registered replications took place in an



sci.5:1702385


online environment where participant’s anonymity was virtually assured. This methodological

adjustment eliminated one of the factors that might encourage hyperfair offers and thus confound

efforts to determine whether religious priming increases prosocial behaviour as measured by the DG.

In Experiment 1, we used the standard DG, following Shariff and Norenzayan’s procedures as closely

as possible. In Experiment 2, we introduced a ‘taking’ option to the standard DG, thereby enabling

dictators to take money from, as well as give money to, the other participant. The procedure used

here followed that of List [30], whose ‘Take $5’ treatment resulted in distributions that appeared

considerably more selfish than those obtained under standard DG conditions. This method,

implemented in an anonymous online setting, provided an experimental context in which prosocial

demand characteristics were lacking, and thus, an environment in which religious priming had ample

opportunity to increase prosocial behaviour.

In these two experiments, however, we went beyond simply seeking to replicate Shariff and

Norenzayan’s 2007 experiments [23]. Most notably, we experimentally evaluated not only Shariff and

Norenzayan’s implicit priming condition but also a commonly used explicit method for activating

religious cognition. For the explicit technique, we had participants write an essay about their beliefs

and feelings about God and their religion, similar to methods used by Inzlicht & Tullett [31] and

McCullough et al. [32]. By exploring two different approaches to religious priming, we sought to

determine (i) which major priming methods increase DG transfers and (ii) whether conscious

awareness of priming materials may underlie any observed effects of implicit priming on DG transfers.

As we noted earlier, multiple priming methods were available. Here, we focused on implicit and

explicit primes, as these are the most common methods used in the literature to date, accounting for

more than 82% of the priming studies surveyed by Shariff et al. [12]. Beyond variation in method,

however, religious primes may differ importantly in content, potentially activating various aspects of

religious identity or other psychological mechanisms that could differentially impact prosocial

behaviour. For instance, some theorists currently emphasize as a likely mechanism the extent to which

individuals view God primarily as punishing and authoritarian, versus benevolent and forgiving

[5,8,9,33–35]. Research has yet to resolve this issue, however, and recent meta-analyses (e.g. [12,19,28])

report effect sizes irrespective of putative mechanism.

Because it remains unclear exactly how religious priming produces its effects—if any—we chose

our primes accordingly. First, we abjured explicit primes that rely upon reading passages (e.g. [36–

38]), as these primes are most likely to vary in terms of what exactly is being primed, and are

subject to extensive researcher interpretation. The essay-based explicit prime that we have chosen

requires participants to write about their idea of religion or God—whatever that may be. Thus, the

essay should have primed whatever aspect of religion was most salient to the participant, rather

than an aspect pre-selected by researchers and imperfectly captured (at best) by a representative

reading passage. Our essay-based prime was therefore well suited to explicitly prime religion to the

same extent as the average explicit priming study included in Shariff et al.’s meta-analysis [12],

where—again—prosocial effects are reported irrespective of mechanism.

What about the choice of our implicit primes? We note that any implicit religious prime is open to the

same charges that can be levied against reading-based explicit primes, namely that the specific target

words may be priming specific aspects of religion that are more or less relevant to specific

psychological mechanisms identified by theory. Here, however, our decision to use the same implicit

primes as Shariff & Norenzayan [23] was justified simply by precedent and the current research

context. Our choice followed from the sheer influence of that particular study, as described earlier,

and secondly from specific questions arising from Gomes and McCullough’s recent failed replication

attempt [29].

Although our priming conditions did not enable us to directly assess whether a particular mechanism

may be driving any observed effects of religious priming upon prosocial behaviour as measured by the

DG, we included an additional measure that enabled us to examine whether a view of God as a

punishing versus benevolent agent moderated any effects we found. This measure was the A/B-God

scale [39], an instrument that asks participants to rate a series of 18 traits from 1 to 7 according to

how much they personally believe each trait accurately characterizes God. The instrument consists of

two sub-scales: the Authoritarian (or ‘A’) sub-scale exemplified by such traits as ‘angry’, ‘punishing’

and ‘wrathful’; and the Benevolent (or ‘B’) sub-scale exemplified by such traits as ‘caring’, ‘merciful’

and ‘forgiving’. With this measure, we tested the prediction that the degree to which participants

view God as authoritarian moderates the effects of religious priming upon DG transfers.

Our experiments were limited in that they did not examine all variables likely relevant to religious

prosociality—including ingroup/outgroup differences and target need state. Nevertheless, published


rsos.royalsocietypublishing.org6


meta-analyses of the effect of religious priming on prosociality that are cited in support of the cultural

evolutionary model and the hypothesis of religious prosociality (e.g. [12]), report effects irrespective of

target need state and ingroup status (as well as of mechanism). Our studies were intended to help

clarify lingering issues with this particular collection of evidence. To be sure, one or two studies

cannot decisively adjudicate the status of such a broad body of experimental evidence, marked by

diverse methods and multiple outcome variables. We put forth the current experiments in the hope

that they will nonetheless constitute a step forward in resolving ongoing uncertainty. In our view, it is

best to build up a body of evidence piece by piece, and thus, we focused on the following empirical

question—‘does religious priming increase DG transfers?’ It is important to answer this question

clearly if theories regarding cultural evolution and religious prosociality are to stand atop a firm

empirical foundation.

R.Soc.opensci.5:170238

2. General method2.1. ParticipantsFor each experiment, we recruited subjects from Amazon’s Mechanical Turk for a 2 (priming condition:

religious versus control) � 2 (priming method: explicit versus implicit) � 2 (religiosity: religious versus

non-religious) between-subjects design. Our pre-registration specified that we would attempt to arrive

at 194 usable subjects (after exclusions) in each of four major groups: (i) subjects given the explicit

religious prime; (ii) subjects given the explicit control prime; (iii) subjects given the implicit religious

prime and (iv) subjects given the implicit control prime. Subjects were required to have a minimum

90% approval rate on MTurk for previously performed tasks and to reside in the USA. For each

subject, we advertised a task offering a modest payment (less than $1.00 guaranteed; see Methods of

each experiment for details) for completing a 15–20 min experiment involving one or more decision-

making tasks, questionnaires and/or writing tasks. Subjects were also informed that they might

receive additional money depending on choices made during the set of decision-making tasks.

A total of 1909 subjects completed the experiments—949 for Experiment 1 and 960 for Experiment

2. Owing to a higher dropout rate in the explicit priming conditions, we ended up with more

implicitly primed subjects per cell than expected (approx. 300 per cell before exclusions, rather than

194), but fewer explicitly primed subjects per cell than expected (approx. 176 per cell before

exclusions, rather than 194). Subjects were excluded in accordance with pre-registered criteria: we

excluded participants whose responses to the suspicion probe indicated suspicion that the study had

to do with a link between religion and prosociality, and we excluded participants who demonstrated

insufficient attention to the task. Altogether, 218 Experiment 1 subjects (23%) and 192 Experiment 2

subjects (20%) were excluded, largely due to incorrect responses in the implicit priming task and to

essays of inadequate length. See electronic supplementary material for details.

2.2. Procedures

2.2.1. Overview

After electing to take part in the experiment and providing informed consent, subjects were randomized

into one of four conditions: implicit religious priming; implicit priming control; explicit religious priming

and explicit priming control.

Subjects completed five basic tasks during the experimental session: (i) the priming task; (ii) the DG;

(iii) a suspicion probe; (iv) a demographic questionnaire, and (v) the A/B-God scale [39]. After

completing their priming task, subjects were given instructions to the DG and asked to make their

DG decision. To guarantee anonymity, subjects were assured that their identity would remain

anonymous to the experimenters and to the other subject, and that there would be no contact between

subjects. After making their decision in the DG, subjects completed a short questionnaire probing for

suspicion, then provided basic demographic data and information on their religious background.

Finally, they completed the A/B-God scale [39].

2.2.2. Priming conditions

The implicit religious priming condition used scrambled sentences that matched those from the religious

prime condition of Study 1 and Study 2 from Shariff & Norenzayan [23]. The implicit priming control



sci.5:1702387


condition likewise used scrambled sentences identical to those from the control condition of Shariff &

Norenzayan, Study 2 [23].

The explicit religious priming condition was based on the religious condition of McCullough et al.,Experiment 1 [32]. Subjects in this condition were asked to write a 5-minute essay about their beliefs

and feelings about God and their religion. To make this condition relevant to non-religious subjects,

those who were not religious were asked to write about what ‘the idea of God’ means to them. In the

explicit control condition, subjects were asked to write a 5-minute essay about the sorts of items they

have in their home, apartment or dormitory room.

Coding for primes was as follows: 1 for religious prime, 0 for neutral prime; 1 for explicit prime and 0

for implicit prime.

See appendix A for the exact wording of all primes.

2.2.3. Religiosity

A categorical indicator of religiosity distinguished religious subjects (coded as 1) from non-religious

subjects (coded as 0). As in Experiment 2 of [23], non-religious subjects were those who identified

themselves as either atheist or agnostic, and who scored below the midpoint of a 7-point scale

assessing belief in God.

2.2.4. Authoritarian view of God

Using participants’ scores on the Authoritarian (A) sub-scale of the A/B-God scale [39], we created a

variable that captured the extent to which subjects viewed God as a punishing, authoritarian figure.

Scores on this variable were computed as the subject’s average endorsement of nine adjectives used

to capture God’s more punitive characteristics, on a scale of 1 to 7. Such adjectives include

‘angry’, ‘punishing’ and ‘wrathful’. Observed authoritarian scores in Experiment 1 ranged from 1 to 7

(M ¼ 3.70, s.d. ¼ 1.36) and did so also in Experiment 2 (M ¼ 3.74, s.d. ¼ 1.35).

2.2.5. Manipulation check

To provide a manipulation check for subjects in the explicit priming conditions, we drew upon the

Linguistic Inquiry and Word Count 2015 software (LIWC2015: [40]). We used LIWC to calculate

the percentage of each essay composed of words in the category of ‘Religion’ (which is pre-defined in

the software). Using an independent groups t-test with a one-tailed a of 0.05, we verified that the

average percentage of religious words in essays composed by religiously primed subjects in both

Experiment 1 (M ¼ 6.24, s.d. ¼ 3.09) and Experiment 2 (M ¼ 6.96, s.d. ¼ 2.92) exceeded the average

percentage of religious words in essays composed by neutrally primed subjects (Experiment 1 M ¼0.05, s.d. ¼ 0.33; Experiment 2 M ¼ 0.03, s.d. ¼ 0.15; Experiment 1 t88.12 ¼ 18.70, s.e. ¼ 0.33, p , 0.001,

Experiment 2 t102.39 ¼ 24.03, s.e. ¼ 0.29, p , 0.001, equal variances not assumed).

2.3. Predictions1A. According to the religious priming hypothesis, there will be a main effect of religious priming, such

that the average DG transfer for all participants receiving a religious prime will be greater than the

average DG transfer for all participants receiving a neutral prime.

1B. According to the religious priming hypothesis, there will be a simple effect of religious priming, such

that the average DG transfer for all participants receiving an implicit religious prime will be greater

than the average DG transfer for all participants receiving an implicit neutral prime.

1C. According to the religious priming hypothesis, there will be a simple effect of religious priming such

that the average DG transfer for all participants receiving an explicit religious prime will be greater

than the average DG transfer for all participants receiving an explicit neutral prime.

2A. According to the religious priming hypothesis as elaborated in [12], there will be a significant

two-way interaction such that the main effect of religious priming (regardless of priming method)

is greater for religious participants than for non-religious participants, and the effect on

non-religious participants will not differ statistically from zero.

2B. According to the religious priming hypothesis, the simple effect of implicit religious priming will be

positive and greater for religious participants than for non-religious participants, and the effect on




sci.5:1702388


2C. According to the religious priming hypothesis, the simple effect of explicit religious priming will be

positive and greater for religious participants than for non-religious participants, and the effect on


3. According to the religious priming hypothesis, the effect of implicit religious priming will remain

robust to removal from the analysis of subjects who report conscious awareness of religious words

during the suspicion probe, as proposed in Gomes & McCullough [28].

4. According to the cultural evolutionary model of religious prosociality, effects of religious priming

will be moderated by the extent to which participants view God as authoritarian.

2.4. Data analysesTo test these predictions, we constructed three generalized linear models (GLMs) in SPSS V.23 for each

experiment, in order to examine the effects of three binary variables—priming condition (religious

versus neutral), priming method (explicit versus implicit) and subject religiosity (religious versus

non-religious)—on DG transfers. In our first model, GLM #1, we included DG transfer as the

dependent variable and priming condition, priming method and religiosity as predictors, along

with all two-way and three-way interactions. In our second model, GLM #2, we included DG

transfer as the dependent variable and the priming condition, subject religiosity and their

interaction as predictors, but only for subjects primed with implicit methods. In our third model,

GLM #3, we included as predictors priming condition, subject religiosity and their interaction, but

only for subjects primed with explicit methods. In electronic supplementary material, tables S7–S10,

we also make available results of exploratory non-parametric tests, which were not pre-registered

and which we conducted to be consistent with Gomes & McCullough [28], who noted that the

distributions of DG transfers are not optimally suited to the assumptions of general linear models

(conclusions were unchanged).

2.4.1. Predictions 1A, 1B and 1C

Prediction 1A (which called for a main effect of religious priming, regardless of priming method) was

evaluated on the basis of the significance of priming condition in GLM #1 (one-tailed a ¼ 0.05).

Prediction 1B (which called for a main effect of implicit religious priming) was evaluated on the basis

of the significance of priming condition in GLM #2. Prediction 1C (which specified a main effect of

explicit religious priming) was evaluated based on the significance of effect for priming condition in

GLM #3 (one-tailed a ¼ 0.05).

With 776 total subjects expected per experiment, we estimated greater than 80% power to detect a

main effect of priming equal to d ¼ 0.18, the bias-corrected estimate of the overall effect of religious

priming on prosociality obtained by Shariff et al. [12]. With 194 subjects per group for the relevant

comparisons, we estimated 90% power to detect a simple effect of implicit or explicit religious

priming at d ¼ 0.30, or 55% power at d ¼ 0.18.

Because our maximum projected sample size for this study fell short of the number of subjects

needed to obtain 90% power to detect main and simple effects of d ¼ 0.18, the risk of obtaining a null

result due to insensitive data was greater than preferred. To address this issue, we pre-registered and

adopted the Bayesian approach of Dienes [41]. As Dienes notes, a study with low a priori power may

end up successfully discriminating between hypotheses; conversely, even high a priori power does not

guarantee that the collected data will actually be sensitive enough to distinguish between hypotheses

[41]. Therefore, it is essential to gauge attained sensitivity and interpret results accordingly. We did so

by computing a Bayes factor, using the online software provided by Dienes [41]. The Bayes factor

represents the probability of the data under the research hypothesis relative to the probability of the

data under the null hypotheses [41]. Although Bayes factors are continuous in nature, we used the

conventional criteria to interpret the computed Bayes factor, with a Bayes factor greater than or equal

to 3 indicating significant evidence in favour of the research hypothesis (that religious priming

increases DG transfers), a Bayes factor less than or equal to 1/3 indicating significant evidence in

favour of the null hypothesis (that religious priming does not increase DG transfers) and a Bayes

factor between 1/3 and 3 indicating insensitive data [41]. For any non-significant result, using the

Bayes factor in this fashion allowed us to determine whether our results provided evidence in favour

of the null, or merely indicated insensitive data—a distinction not possible with null hypothesis

significance testing, even with high a priori power.



sci.5:1702389


To compute a Bayes factor using the tools of Dienes [41], we had to specify a plausibility distribution of

effects predicted by the research hypothesis. (We defined the effect of religious priming as the difference

between the average DG transfer made by subjects receiving a religious prime and the average DG game

transfer made by subjects receiving a neutral prime.) Dienes offers three commonly encountered

scenarios, all of which were adapted to the present circumstance: a uniform distribution for the predicted

effects; a normal distribution for the predicted effects and a half-normal distribution for the predicted

effects [41]. With a uniform distribution, all predicted effects between a minimum and maximum value

are assumed to be equally likely under the research hypothesis. In our case, the research hypothesis

predicted that religious priming would increase the DG transfers of sufficiently selfish individuals, but

not lead to ‘hyperfair’ offers [29, p. 105] greater than 50% of endowment. Thus, we modelled the

predicted effect of the research hypothesis as a uniform distribution ranging from 0 (no difference in DG

transfers) to (1/2 endowment minus the average DG transfer for neutrally primed subjects). The second

distribution of predicted effects suggested by Dienes is a normal distribution, with values centred on a

most likely estimate and decreasing in probability as they diverge to either side of that point estimate.

Because the research hypothesis predicts both a zero probability of an effect size less than zero and a

zero probability of an effect size greater than the difference between half-endowment and the average

transfer of neutrally primed subjects, we centred the normal distribution upon the midway point

between zero (no difference in DG transfers) and (1/2 endowment minus the average DG transfer for

neutrally primed subjects). Following Dienes [41], the standard deviation of this normal distribution was

1/2 of the value of the midpoint, effectively ensuring a zero probability for a predicted effect size of zero

and a zero probability for a predicted effect resulting in the transfer of half of the subject’s endowment.

The third and final distribution of predicted effects suggested by Dienes is the half-normal distribution,

which assumes that effects near zero are more likely than effects closer to a maximal value [41]. We

modelled the religious priming hypothesis using the half-normal distribution as follows: the modal

effect size of the predicted distribution was set at zero. As predicted effect sizes increase above zero, they

become increasingly less likely, until they reach zero probability when the effect results in a transfer of

1/2 the subject’s endowment—the maximal value. Following Dienes [41], to achieve this distribution

of predicted effects, we set the standard deviation of this half-normal distribution equal to 1/2 the

difference between zero (no difference in DG transfers) and (1/2 endowment minus the average DG

transfer for neutrally primed subjects). To ensure that our results were robust to differences in modelling

the predicted effects, we report Bayes factors computed with the three approaches detailed above.

2.4.2. Predictions 2A, 2B and 2C

Prediction 2A (which called for the main effect of religious priming across all priming methods to be

positive among religious participants, but zero among non-religious participants) was evaluated on

the basis of the significance of the interaction between priming condition and subject religiosity in

GLM #1. Prediction 2B (which called for the main effect of implicit religious priming to be positive

among religious participants, but zero among non-religious participants) was evaluated on the basis

of the significance of the interaction between priming condition and subject religiosity in GLM #2.

Prediction 2C (which called for the main effect of explicit priming to be positive among religious

participants, but zero among non-religious participants) was evaluated on the basis of the significance

of the interaction between priming condition and subject religiosity in GLM #3. All of these

evaluations were performed using a one-tailed a of 0.05.

Because power was less than 90% to detect the interaction, we again used the Bayesian methods of

Dienes [41] to interpret results. We defined the interaction effect as the effect of a religious prime

upon religious subjects (dr) minus the effect of a religious prime upon non-religious subjects (dn).

Interpreted conservatively, the research hypothesis predicts that the effect of a religious prime upon

religious subjects will exceed its effect upon non-religious subjects (dr . dn). On the research

hypothesis, the range of values for the interaction effect thus runs from 0 (when dn ¼ dr) to dr (when

dn ¼ 0). Following the example of Dienes [41], we therefore pre-registered that we would use a

uniform distribution ranging from 0 to dr to model the plausibility distribution of the interaction effect

predicted by the research hypothesis. And we again used the conventional criteria to interpret the

resulting Bayes factor for this interaction [41].

We note here, however, that subsequent Bayesian analyses of interaction effects revealed limitations

with our pre-registered analytical strategy for modelling the predictions of the priming hypothesis, at

least where interactions were concerned. Specifically, our pre-registered strategy defined the plausibility

distribution of the interaction effect by using a uniform distribution that ranged from a lower bound of



sci.5:17023810


zero to an upper bound equal to the priming effect that was actually observed among religious subjects.

This approach assumed—consistent with the priming hypothesis—that this effect would be positive,

but subsequent analyses involving implicit priming revealed that assumption to be flawed. In cases

where the priming effect among religious subjects was negative, our procedure yielded a Bayes factor of

0.00, indicating that the data were literally impossible under the research hypothesis.

Such a result led us to seek a more appropriate method for modelling the plausibility distribution of

the interaction effect, one in which the upper bound of the plausibility distribution would always be

positive and would more realistically reflect the predictions of the priming hypothesis. It seemed to us

that the most straightforward post hoc approach was to take full advantage of a clear prediction made

in recent formulations of the priming hypothesis—namely, that religious priming has no effect on

non-religious subjects. If that is the case, then the interaction effect should be equal to the possible

effect of religious priming among religious subjects only. This possible effect, in turn, would best be

modelled as a uniform distribution with a lower bound of 0 (reflecting no difference in DG transfers

between religiously primed and neutrally primed religious subjects) and an upper bound equal to the

maximum expected size (rather than observed size) of the priming effect among religious subjects. For

Experiment 1, this difference would be the difference between 50 cents (0.50) and the mean DG

transfer of neutrally primed religious subjects. For Experiment 2, this difference would be the

difference between 25 cents (0.25) and the mean DG transfer of neutrally primed religious subjects. In

view of the problems with our pre-registered strategy for analysing the interaction effect, we report in

the main text only these post hoc Bayesian analyses of the interaction, along with Bayesian analyses of

the simple effects of priming at the levels of both religious and non-religious subjects. However, we

also provide results of our pre-registered Bayesian analyses of the interaction in the appropriate

summary tables (tables 2 and 5; electronic supplementary material, tables S2 and S5).

2.4.3. Prediction 3

We pre-registered that we would test Prediction 3 (which called for the effect of implicit religious priming

to be robust to the removal of subjects who report conscious awareness of religious words during the

suspicion probe) only if Prediction 1B or Prediction 2B was supported. Because neither Prediction 1B

nor Prediction 2B was supported, we performed no analyses bearing on Prediction 3.

2.4.4. Prediction 4

To test Prediction 4, we conducted moderation analyses using the A/B-God measure [39], restricting our

sample to Christian respondents because the measure has been validated using only a Christian sample.

Using this restricted sample, we constructed three GLMs, each of which regressed DG transfers on

priming condition, with subjects’ scores on the Authoritarian (A) sub-scale entered as a moderator.

There were three such analyses—one for all Christian subjects (GLM #4), one for Christian subjects

receiving an implicit prime (GLM #5) and one for Christian subjects receiving an explicit prime

(GLM #6). Results were evaluated based on the significance of the interaction between priming

condition and Authoritarian score in the corresponding GLM. All of these evaluations were

performed using a one-tailed a of 0.05.

We had planned, in the case of null results, to employ the Bayesian methods of Dienes [41], as follows.

First, we specified the two levels of score on the Authoritarian sub-scale at which to compare the effects.

We defined the ‘High’ level of Authoritarian score as one standard deviation above the mean, and we

defined the ‘Low’ level of Authoritarian score as one standard deviation below the mean. We then

defined the interaction effect as the effect of a religious prime at the level of ‘High’ Authoritarian score

(dH) minus the effect of a religious prime at the level of ‘Low’ Authoritarian score (dL).

The research hypothesis predicts that the effect of a religious prime at the ‘High’ level of

Authoritarian score will be greater than that of a religious prime at the ‘Low’ level of Authoritarian

score (dH . dL). On the research hypothesis, the range of values for the interaction effect thus runs

from 0 (when dH ¼ dL) to dH (when dL ¼ 0). Following the example of Dienes [41], we therefore pre-

registered that we would use a uniform distribution ranging from 0 to dH to model the plausibility

distribution of the interaction effect predicted by the religious priming hypothesis, and we used the

previously described conventional criteria to interpret the resulting Bayes factor for this interaction.

We note here some shortcomings of our pre-registered strategy. First, in requiring us to split the

sample into two groups based on authoritarian views of God, and then to compare the effects of

religious priming across those two groups, our pre-registered strategy entailed that we eliminate a



sci.5:17023811


substantial proportion of subjects from consideration and evaluate our primary analysis on the basis of a

second, less powerful analysis. Our pre-registered Bayesian analytical approach was also challenged by

how we specified the plausibility distribution of the interaction effect predicted by the priming

hypothesis. This is the same issue we encountered when describing the data analyses for Predictions

2A–2C: our pre-registered strategy assumed that DG transfers among religiously primed subjects

would be greater than transfers among neutrally primed subjects, and that this positive difference

would then form the upper bound for the uniform distribution used to model the predicted

interaction effect. For implicitly primed subjects, however, mean DG transfers were lower among

religiously primed subjects than among neutrally primed subjects, rendering our planned strategy

arguably problematic in that the upper bound of the uniform distribution was lower than the lower

bound, returning a Bayes factor of 0.00. To address this concern, we conducted an additional set of

non-pre-registered Bayesian analyses. In this alternative approach, and similar to what we described

above for Predictions 2A–2C, we modelled the upper bound of the plausibility distribution of the

interaction effect as the expected rather than the observed range of the religious priming effect

among subjects with a highly authoritarian view of God—a value that would always be positive. For

Experiment 1, this upper bound was $0.50 minus the average DG transfer among neutrally primed

High Authoritarians. For Experiment 2, this upper bound was $0.25 minus the average DG transfer

among neutrally primed High Authoritarians. Given the issues with our pre-registered strategy for

analysing the interaction effect, we address in the main text only these post hoc Bayesian analyses of

the interaction. However, we do make available the results of our pre-registered Bayesian analyses

of the interaction (in tables 3 and 6; electronic supplementary material, tables S3 and S6).

Pre-registration. After this replication proposal was accepted but before any data were collected, we

pre-registered both experiments on the Open Science Framework (https://osf.io/6nqwt/).

3. Overview of key findingsTo help readers navigate our rather extensive results, below we provide a brief summary of essential

findings from the experiments, and we direct readers to the locations of the relevant supporting evidence.

The key findings are as follows:

(1) Religious participants transferred more money in the DGs than did non-religious participants. (For support,

see the subsections entitled ‘Did religious subjects transfer more money than did non-religious

subjects?’ in the Results section of both Experiment 1 and Experiment 2 (§§4.4 and 5.5).)

(2) Implicit religious priming did not increase DG transfers among either religious or non-religious participants.(For support, see the subsections entitled ‘Prediction 2B’ in the Results section of both Experiment 1

and Experiment 2 (§§4.9 and 5.10).)

(3) A new meta-analysis of all available studies reinforces the conclusion that implicit religious priming does notincrease DG transfers. (For support, see the Study 3 Results section (§6.1)).

(4) Explicit religious priming did not appear to increase DG transfers among non-religious participants, but mayhave increased transfers among religious participants, with a small effect size. (For support, see the

subsections entitled ‘Prediction 2C’ in the Results section of both Experiment 1 and Experiment 2,

as well as the subsection of the General discussion entitled ‘Implicit versus explicit religious

priming’ (§§4.10, 5.11 and 7.2).)

4. Experiment 14.1. SubjectsThe mean age of the sample was 35.54 years (s.d. ¼ 11.50). A sizable majority of participants (80.0%)

identified as White, 54.6% of participants were female and 38.4% of participants characterized

themselves as either atheist or agnostic.

4.2. ProceduresIn Experiment 1, subjects took part in a standard DG. We offered each subject a payment of $0.30 plus

$0.50 in bonus money. This $0.80 payment was in addition to money that they kept based on their DG

decision. Subjects were told that they would be the ‘giver’ in an economic decision-making task, were

https://osf.io/6nqwt/



Table 1. Descriptive statistics for DG transfers in Experiment 1.

priming method religiosity

priming conditionmean+ s.d. (N )

control religious priming total

explicit non-religious 0.219+ 0.252

(48)

0.270+ 0.304

(33)

0.240+ 0.273

(81)

religious 0.319+ 0.268

(104)

0.378+ 0.343

(55)

0.340+ 0.296

(159)

total 0.288+ 0.266

(152)

0.338+ 0.331

(88)

0.306+ 0.292

(240)

implicit non-religious 0.238+ 0.274

(90)

0.228+ 0.291

(83)

0.233+ 0.281

(173)


(160)

0.290+ 0.286

(147)

0.300+ 0.276

(316)

total 0.284+ 0.271

(259)

0.267+ 0.289

(230)

0.276+ 0.279

(489)

total non-religious 0.231+ 0.266

(138)

0.240+ 0.294

(116)

0.235+ 0.278

(254)


(273)

0.314+ 0.305

(202)

0.313+ 0.283

(475)

total 0.285+ 0.269

(411)

0.287+ 0.302

(318)

0.286+ 0.284

(729)


sci.5:17023812


provided a $1.00 endowment and were asked how much of the endowment, if any, they wished to

transfer to a second, anonymous subject. Our DG instructions followed the procedure of Shariff and

Norenzayan’s 2007 experiments as closely as possible, while adapting the instructions to the online

environment as necessary and slightly modifying the wording so as not to deceive subjects. See

appendix B for a detailed comparison of the DG instructions and questionnaire items used in this

experiment versus those used by Shariff & Norenzayan [23].

4.3. ResultsDescriptive statistics appear in table 1; means reported in the text are estimated marginal means.

A summary of all Experiment 1 results appears in tables 2 and 3. In the main text, we report only the

results obtained using the pre-registered exclusion criteria, but we note where conclusions differ if

analyses are conducted without excluding any subjects (see electronic supplementary material, table

S2 for specifics). Where indicated, p-values have been adjusted to reflect one-tailed hypothesis tests.

For effect sizes involving mean differences, we report Hedges’ g. We standardized g by using the

overall error term from the relevant linear model, and we corrected for bias using the procedure

recommended by Borenstein et al. [42]. We report R2change as the effect size for analyses testing whether

the effect of religious priming is moderated by the extent to which subjects view God as a punishing,

authoritarian figure.

4.4. Did religious subjects transfer more money than did non-religious subjects?Yes. Our results focus on the effects of priming condition and on possible interactions of priming

condition with subject religiosity. We would be remiss, however, if we failed to mention at the outset

that we observed a persistent main effect of subject religiosity in Experiment 1, evident among

implicitly primed subjects, F1,485 ¼ 6.39, p ¼ 0.012 (two-tailed; GLM #2); among explicitly primed

subjects, F1,236 ¼ 6.64, p ¼ 0.011 (two-tailed; GLM #3) and among all subjects considered regardless of


Tabl

e2.

Expe

rimen

t1re

sults

sum

mar

y:pr

imin

gef

fects

and

inte

ractio

nsw

ithre

ligios

ity.

pred

iction

mod

elm

ean

diffe

renc

es.e

.p- va

lues

an1 pr

ime

n2 cont

rol

gs.e

.of

gBa

yes

unifo

rmBa

yes

norm

alBa

yes

1/2

norm

al

1A:p

rimin

gef

fect,

all

met

hods

and

subj

ects

GLM

#10.

020

0.02

40.

199

318

411

0.07

0.07

0.30

0.17

0.45

1B:i

mpl

icitp

rimin

gef

fect,

all

subj

ects

GLM

#22

0.01

50.

026

0.70

923

025

92

0.05

0.09

0.10

0.06

0.15

1C:e

xplic

itpr

imin

gef

fect,

all

subj

ects

GLM

#30.

055

0.04

10.

088

8815

20.

190.

131.

000.

991.

34

2A:i

nter

actio

nw

ithre

ligios

ity,

allm

etho

ds

GLM

#12

0.00

050.

047

0.50

431

841

1—

—0.

97b

n.a.

n.a.

2A:i

nter

actio

nw

ithre

ligios

ity,

allm

etho

ds

GLM

#12

0.00

050.

047

0.50

431

841

1—

—0.

32c

n.a.

n.a.

2A:p

rimin

gef

fect,

all

met

hods

,reli

giou

ssu

bs

only

GLM

#10.

020

0.02

80.

241

202

273

0.07

0.09

0.37

0.27

0.55

2A:P

rimin

gef

fect,

all

met

hods

,non

-relig

ious

subs

GLM

#10.

020

0.03

80.

298

116

138

0.07

0.13

0.28

0.18

0.43

2B:i

nter

actio

nw

ithre

ligios

ity,

impl

icitp

rimin

gon

ly

GLM

#22

0.00

90.

053

0.56

823

025

9—

—0.

00d

n.a.

n.a.

2B:i

nter

actio

nw

ithre

ligios

ity,

impl

icitp

rimin

gon

ly

GLM

#22

0.00

90.

053

0.56

823

025

9—

—0.

30c

n.a.

n.a

2B:i

mpl

icitp

rimin

gef

fect,

relig

ious

subs

only

GLM

#22

0.01

90.

031

0.72

814

716

92

0.07

0.11

0.13

0.09

0.21 (C

ontin

ued.

)


sci.5:17023813



Tabl

e2.

(Con

tinue

d.)

pred

iction

mod

elm

ean

diffe

renc

es.e

.p- va

lues

an1 pr

ime

n2 cont

rol

gs.e

.of

gBa

yes

unifo

rmBa

yes

norm

alBa

yes

1/2

norm

al

2B:i

mpl

icitp

rimin

gef

fect,

non-

relig

ious

subs

only

GLM

#22

0.01

00.

042

0.59

483

902

0.04

0.15

0.17

0.11

0.26

2C:i

nter

actio

nw

ithre

ligios

ity,

expl

icitp

rimin

gon

ly

GLM

#30.

008

0.08

00.

461

8815

2—

—0.

95b

n.a.

n.a.

2C:i

nter

actio

nw

ithre

ligios

ity,

expl

icitp

rimin

gon

ly

GLM

#30.

008

0.08

00.

461

8815

2—

—0.

58c

n.a.

n.a.

2C:e

xplic

itpr

imin

gef

fect,

relig

ious

subs

only

GLM

#30.

059

0.04

80.

111

5510

40.

200.

171.

251.

381.

46

2C:e

xplic

itpr

imin

gef

fect,

non-

relig

ious

subs

only

GLM

#30.

051

0.06

50.

218

3348

0.18

0.23

0.62

0.60

0.83

a p-va

lues

are

one-

taile

d.b Ba

yes

facto

rrefl

ects

orig

inal

pre-

regi

stere

dan

alytic

alstr

ateg

y.c Ba

yes

facto

rrefl

ects

revis

edan

alytic

alstr

ateg

y.d Ba

yes

facto

rrefl

ects

orig

inal

pre-

regi

stere

dan

alytic

alstr

ateg

y,bu

tthe

relig

ious

prim

ing

effe

ctwa

sin

the

dire

ction

coun

tert

oth

eory

,ren

derin

gco

mpu

tatio

nof

the

Baye

sfac

toru

sing

pre-

regi

stere

dm

etho

dspr

oblem

atic.


sci.5:17023814



Table 3. Experiment 1 results summary: interactions with view of God as authoritarian figure.

prediction modelmeandifference s.e. p-valuea

Bayes factoruniform

‘A’ scale moderates priming effect,

all methods

GLM #4 0.032 0.024 0.093 1.08b


all methods

GLM #4 0.032 0.024 0.093 2.02c


implicit priming only

GLM #5 0.037 0.030 0.104 0.00d



GLM #5 0.037 0.030 0.104 3.14c


explicit priming only

GLM #6 0.049 0.043 0.128 1.36b



GLM #6 0.049 0.043 0.128 1.39c

ap-values are one-tailed.bBayes factor calculated from High/Low split.cBayes factor calculated from High/Low split; non-pre-registered analytical approach.dBayes factor calculated from High/Low split; the priming effect among subjects with highly authoritarian views of God was inthe direction counter to theory, rendering computation of the Bayes factor using pre-registered methods problematic.


sci.5:17023815


priming method, F1,721 ¼ 12.85, p , 0.001 (two-tailed; GLM #1). Across all subjects, religious subjects

transferred 8.6 cents more on average than did non-religious subjects, g ¼ 0.30 95% CI [0.15, 0.46].

4.5. Prediction 1A: Was there a main effect of religious priming, such that the average DictatorGame transfer for all subjects receiving a religious prime (regardless of priming method)exceeded the average Dictator Game transfer for all subjects receiving a neutral prime?

No. Results from GLM #1 indicated that the DG transfers of religiously primed subjects (M ¼ $0.291) did

not significantly differ from those of subjects who received a neutral prime (M ¼ $0.271), t721 ¼ 0.846,

s.e. ¼ 0.024, p ¼ 0.199 (one-tailed), g ¼ 0.07, 95% CI [20.07, 0.22].

Bayesian analyses of the non-significant finding generally favoured the null hypothesis, but

conclusions resulting from the Bayes factors depended upon how the predictions of the priming

hypothesis were modelled. When computed using uniform and normal distributions for the predicted

priming effect, the Bayes factors were 0.30 and 0.17, respectively, offering reasonable evidence for

the null. But, the data did not provide reasonable evidence for the null hypothesis when we used a

half-normal distribution to model the expected effect (Bayes factor ¼ 0.45).

4.6. Prediction 1B: Was there a simple effect of implicit religious priming, such that theaverage Dictator Game transfer for all subjects receiving an implicit religious primeexceeded the average Dictator Game transfer for all subjects receiving an implicitneutral prime?

No. GLM #2 indicated that the DG transfers of subjects who received an implicit religious prime

(M ¼ $0.259) were not significantly greater than those of subjects who received an implicit neutral

prime (M ¼ $0.273), t485 ¼ 20.553, s.e. ¼ 0.026, p ¼ 0.709 (one-tailed), g ¼ 20.05, 95% CI [20.23, 0.12].

Indeed, inspection of both the estimated marginal means and the descriptive means indicated that, if

anything, subjects receiving an implicit religious prime transferred less money than did those

receiving an implicit neutral prime.



sci.5:17023816


Bayesian analyses of the non-significant finding supported the null hypothesis regardless of how the

predictions were modelled, with Bayes factors ranging from 0.06 (normal distribution) to 0.15

(half-normal distribution). These findings constitute evidence that implicit religious priming did not

increase DG transfers, regardless of subjects’ religiosity.

4.7. Prediction 1C: Was there a simple effect of explicit religious priming such that the averageDictator Game transfer for all subjects receiving an explicit religious prime exceeded theaverage Dictator Game transfer for all subjects receiving an explicit neutral prime?

No. GLM #3 indicated that the DG transfers of subjects who received an explicit religious prime

(M ¼ $0.324) were not significantly greater than those of subjects who received an explicit neutral

prime (M ¼ $0.269), although the results approached significance using the one-tailed test, t236 ¼ 1.355,

s.e. ¼ 0.041, p ¼ 0.088 (one-tailed), g ¼ 0.19, 95% CI [20.07, 0.45].

Consistent with the marginally significant results revealed by the t-test, Bayesian analyses indicated

that the data did not distinguish the null from the priming hypothesis, with Bayes factors ranging from

0.99 (normal distribution) to 1.34 (half-normal distribution). It thus remains unclear whether explicit

priming influenced DG transfers in Experiment 1, considering subjects irrespective of their religiosity.

4.8. Prediction 2A: Was there a significant two-way interaction such that the main effect ofreligious priming (regardless of priming method) was greater for religious subjects thanfor non-religious subjects, and the effect on non-religious subjects did not differstatistically from zero?

No. Results from GLM #1 indicated that the interaction of priming condition with subject religiosity was

not significant, t721 ¼ 20.01, s.e. ¼ 0.047, p ¼ 0.504 (one-tailed). There was no significant effect of

religious priming, either for religious subjects (t721 ¼ 0.71, s.e. ¼ 0.028, p ¼ 0.241 (one-tailed), g ¼ 0.07,

95% CI [20.11, 0.25]) or for non-religious subjects (t721 ¼ 0.53, s.e. ¼ 0.038, p ¼ 0.298 (one-tailed),

g ¼ 0.07, 95% CI [20.18, 0.32]).

Using our revised approach to analyse interaction effects (see Data analyses), we obtained a Bayes

factor for the interaction of 0.32, slightly favouring the null hypothesis that there is no interaction. For

the simple effect of priming on specifically religious subjects, Bayesian analyses did not provide clear

evidence against the priming effect, with Bayes factors ranging from 0.27 (normal distribution) to 0.55

(half-normal distribution). Similarly, Bayesian analyses of the simple effect of priming on specifically

non-religious subjects failed to furnish clear evidence against the priming effect, with Bayes factors

ranging from 0.18 (normal distribution) to 0.43 (half-normal distribution).

4.9. Prediction 2B: Among implicitly primed subjects only, was there a significant interactionbetween priming condition and religiosity, such that the simple effect of implicit religiouspriming was positive and greater for religious subjects than for non-religious subjects, andthat the effect on non-religious subjects did not differ statistically from zero?

No. The interaction of priming condition with subject religiosity was not significant in GLM #2,

t485 ¼ 20.171, s.e. ¼ 0.053, p ¼ 0.568 (one-tailed). There was no significant effect of implicit religious

priming, either for religious subjects (t485 ¼ 20.61, s.e. ¼ 0.031, p ¼ 0.728 (one-tailed), g ¼ 20.07, 95%

CI [20.29, 0.15]), or for non-religious subjects (t485 ¼ 20.24, s.e. ¼ 0.042, p ¼ 0.594 (one-tailed),

g ¼ 20.04, 95% CI [20.33, 0.26]).

Using the revised approach to conduct Bayesian analyses of this interaction (detailed in Data

Analyses), we computed a Bayes factor of 0.30, slightly favouring the null hypothesis of no interaction

(though this conclusion is perhaps qualified by the fact that the Bayes factor was 0.40 when we

analysed the full sample, with no exclusions). When we turned to the simple effect of implicit

religious priming among specifically religious subjects, Bayesian analyses provided evidence that

implicit religious priming did not affect DG transfers, with Bayes factors ranging from 0.09 (normal

distribution) to 0.21 (half-normal distribution). For non-religious subjects, Bayesian analyses likewise

furnished clear evidence that implicit religious priming had no effect, with Bayes factors ranging from



sci.5:17023817


0.11 (normal distribution) to 0.26 (half-normal distribution). These Bayesian analyses thus indicate that

the effect of implicit religious priming did not differ based on the religiosity of the subjects, and that

the priming effect was zero rather than positive for both non-religious and religious subjects.

4.10. Prediction 2C: Among explicitly primed subjects only, was there a significant interactionbetween priming condition and religiosity, such that the simple effect of explicit religiouspriming was positive and greater for religious subjects than for non-religious subjects,and that the effect on non-religious subjects did not differ statistically from zero?

No. In GLM #3, the interaction of priming condition with subject religiosity was not significant, t236 ¼

0.099, s.e. ¼ 0.081, p ¼ 0.461 (one-tailed). There was no significant effect of explicit religious priming

either for religious subjects (t236 ¼ 1.23, s.e. ¼ 0.048, p ¼ 0.111 (one-tailed), g ¼ 0.20, 95% CI [20.12,

0.53]) or for non-religious subjects (t236 ¼ 0.79, s.e. ¼ 0.065, p ¼ 0.218 (one-tailed), g ¼ 0.18, 95% CI

[20.27, 0.62]).

For the interaction, Bayesian analyses conducted using our revised methods (see Data analyses) were

unable to distinguish the null hypothesis from the priming hypothesis, yielding a Bayes factor equal to

0.58, indicating insensitive data. When we examined the simple effect of explicit priming on specifically

religious subjects, Bayesian analyses did not provide reliable evidence in favour of the null hypothesis,

with Bayes factors ranging from 1.25 (uniform distribution) to 1.46 (half-normal distribution).

Similarly, Bayesian analyses of the simple effect of explicit priming on specifically non-religioussubjects failed to furnish reliable evidence in favour of the null hypothesis, with Bayes factors ranging

from 0.60 (normal distribution) to 0.83 (half-normal distribution). The data thus did not arbitrate

against an effect of explicit religious priming, particularly for religious subjects, nor did the data

suggest that the effect of explicit priming does not differ between religious and non-religious subjects.

4.11. Prediction 3: Did the effect of implicit religious priming remain significant even afterremoving from the analysis any subjects who reported conscious awareness of religiouswords during the suspicion probe?

Because there were no significant effects of implicit religious priming, we did not perform this analysis.

4.12. Prediction 4: Were the effects of religious priming moderated by the extent to whichsubjects viewed God as authoritarian?

After exclusions for suspicion, essay length and inattention to the task, 325 subjects who identified as

Christian remained in the Experiment 1 sample. These 325 subjects formed the basis for the following

moderation analyses, as stipulated in the pre-registration. To test for moderation of the religious

priming effect, we regressed DG transfers on experimental condition, subject’s authoritarian view of

God and the interaction of the two.

GLM #4: Results for all Christian subjects, regardless of priming method. When we regressed DG transfers

on priming condition, subject’s authoritarian view of God and the interaction of the two, the interaction

was not significant t321 ¼ 1.33, s.e. ¼ 0.024, p ¼ 0.093 (one-tailed), R2change ¼ 0:005.

To conduct our Bayesian analyses, we first split the sample of Christian subjects into two groups

based on authoritarian views of God, as described previously. After we did so, 113 subjects remained,

with individual cell sizes ranging from 22 (for religiously primed subjects with highly authoritarian

views of God) to 40 (for neutrally primed subjects with low authoritarian views of God). Using this

subsample, we regressed DG transfers on priming condition, the grouping variable that captured

authoritarian views of God (High versus Low), and the interaction of the two. There was again no

significant interaction between authoritarian views of God and priming condition, t109 ¼ 0.140, s.e. ¼

0.024, p ¼ 0.083 (one-tailed). Bayesian analyses of this non-significant interaction (using our revised

analytical strategy—see Data analyses) returned a Bayes factor of 2.02.

GLM #5: Results for implicitly primed Christian subjects. We then tested for moderation of the religious

priming effect using only implicitly primed Christian subjects (N ¼ 209). We again regressed DG

transfers on priming condition, subject’s authoritarian view of God and the interaction of the two.

The interaction was not significant, t205 ¼ 1.26, s.e. ¼ 0.030, p ¼ 0.104 (one-tailed), R2change ¼ 0:008.



sci.5:17023818


To conduct Bayesian analyses of this non-significant result, we again split the sample of Christian

subjects into two groups based on authoritarian views of God. After we did so, 66 subjects remained,

with individual cell sizes ranging from 11 (for neutrally primed subjects with highly authoritarian


subsample to regress DG transfers on priming condition, the grouping variable that captured

authoritarian views of God (High versus Low), and the interaction of the two, we found a significant

interaction between authoritarian views of God and priming condition, t62 ¼ 1.70, s.e. ¼ 0.126, p ¼0.048 (one-tailed). Using our alternative approach to specify the plausibility distribution of the

interaction effect predicting by the priming hypothesis (see Data analyses), we obtained a Bayes factor

of 3.14.

This latter Bayes factor provides evidence that the effect of implicit religious priming varies between

subjects who view God as highly authoritarian versus those who view God as low in authoritarianism. In

probing the significant interaction, we found that the interaction appears to be driven largely by subjects

who view God as low in authoritarianism (Low Authoritarians). The average transfer of neutrally primed

Low Authoritarians (M ¼ 0.419) was 23.1 cents greater than that of Low Authoritarians who had been

religiously primed (M ¼ 0.188), p ¼ 0.006, two-tailed. The direction of the implicit religious priming

effect among Low Authoritarians seems to us contrary to the predictions of the religious priming

hypothesis. Religiously primed High Authoritarians (M ¼ 0.165) transferred 1.2 cents less than

neutrally primed High Authoritarians (M ¼ 0.182), a difference that was not statistically significant,

p ¼ 0.860, two-tailed. Whatever this interaction may represent, it therefore does not appear to indicate

that implicit religious priming causes subjects who view God as strongly authoritarian to transfer

more money to recipients, while having less of an effect on subjects who view God as weakly

authoritarian. Instead, if anything, the interaction would suggest that implicit religious priming causes

subjects who view God as low in authoritarianism to give less money to recipients, while having little

or no effect on subjects who view God as high in authoritarianism.

GLM #6: Results for explicitly primed Christian subjects. When we tested for moderation of the religious

priming effect using only explicitly primed Christian subjects (N ¼ 116), our regression of priming

condition, authoritarian view of God and the interaction of the two yielded a non-significant

interaction, t112 ¼ 1.14, s.e. ¼ 0.043, p ¼ 0.128 (one-tailed), R2change ¼ 0:011.

To interpret this null result using Bayesian methods, we split the sample of Christian subjects into

two groups based on authoritarian views of God. This resulted in a subsample of 47 subjects, with

individual cell sizes ranging from 5 (for religiously primed subjects with highly authoritarian views of

God) to 19 (for neutrally primed subjects with low authoritarian views of God). When we regressed

DG transfers on priming condition, the grouping variable that captured authoritarian views of God

(High versus Low), and the interaction of the two, we found that the interaction was not significant,

t43 ¼ 0.923, s.e. ¼ 0.193, p ¼ 0.181 (one-tailed). Bayesian analyses using our revised analytical strategy

(see Data analyses) indicated that the data were insensitive rather than supportive of the null (Bayes

factor ¼ 1.39), an unsurprising outcome given the small sample size.

Assessing all of the moderation analyses for Experiment 1 collectively, we thus find that the

data—despite multiple non-significant results—do not argue against the possibility that the effect

of religious priming among Christian subjects varies as a function of how authoritarian the subject

understands God to be.

4.13. DiscussionExperiment 1 tested the hypothesis that religious priming increases DG transfers, using the standard DG

paradigm and two common approaches to implicit and explicit religious priming. We consistently

obtained non-significant results, which we interpreted using Bayesian analyses in order to determine

whether the data actually supported the null hypothesis, or were merely insensitive. When we

collapsed results across the priming method and considered subjects without regard to their

religiosity, we found that the data tended to favour the null hypothesis, but were ultimately

inconclusive: results were not robust to how we modelled the expected distribution of the priming effect.

Breaking down the results by priming method, however, yielded a clearer picture. We found strong

evidence that implicit priming did not increase DG transfers, considering subjects without regard to their

religiosity. Indeed, the implicit priming data of Experiment 1 were anywhere from 6 to 16 times more

likely under the null hypothesis than under the research hypothesis. Furthermore, Experiment 1

supported the null hypothesis among both religious and non-religious subjects who were implicitly

primed, and provided evidence that the effect of implicit priming does not vary based on subject


rsos.royalsocietypublishing.or19


religiosity. The same cannot be said for explicit priming, where the data failed to provide a decisive outcome,

despite multiple non-significant results. Our results therefore do not arbitrate against the possibility that

explicit religious priming increases DG transfers, particularly among religious individuals, with a small

effect size (the point estimate for explicit priming among religious individuals was 0.20).

In Experiment 1, we had the additional goal of testing whether the effect of religious priming (among

Christian subjects only) was moderated by the extent to which subjects viewed God as a punishing,

authoritarian figure. Although we obtained non-significant results, Bayesian analyses indicated that

we did not have meaningful evidence in favour of the null hypothesis. Experiment 1 therefore offered

no evidence against the possibility that the effect of religious priming among Christian subjects varies

as a function of how punishing or authoritarian they understand God to be.
gR.Soc.open
sci.5:170238

5. Experiment 2Experiment 1 relied upon the standard DG paradigm. Because prior theorizing suggested that religious

priming might increase DG transfers only when baseline levels of selfishness are sufficiently high [29],

Experiment 2 featured a modified DG that allowed dictators the option of taking money fromrecipients, as well as transferring money to them. In other respects, the procedures were essentially

identical to those of Experiment 2.

5.1. SubjectsSubject payment was identical to Experiment 1, save that the bonus amount was $0.45 rather than $0.50.

The mean age of the sample was 36.39 years (s.d. ¼ 11.95). A sizable majority of participants (80.3%)

identified as White, 53.5% of participants were female and 38.2% of participants characterized

themselves as either atheist or agnostic.

5.2. ProceduresExperiment 2 paralleled Experiment 1 quite closely, varying only in the nature of the DG used. Rather

than the standard DG, Experiment 2 relied upon a modified DG with a ‘take’ option, as in the ‘Take

$5’ treatment of List [30]. Each dictator was informed that he or she was being paired with another

anonymous subject on MTurk, and that both subjects had been endowed with $0.50. Dictators were

further informed that they, but not the other subject, had been provisionally endowed with an

additional $0.50. Dictators were then asked what portion of this provisional endowment, if any, they

wished to transfer to the other subject. Dictators were told that they can also transfer a ‘negative

amount—i.e. you can take up to $0.50 from the other subject’. Transfers could occur in increments of

$0.10, and thus ranged from 2$0.50 (i.e. $0.50 taken from the other player) to $0.50 (i.e. $0.50 given to

the other player). See appendix C for the exact wording of the DG procedure in Experiment 2.

5.3. Hypotheses and data analysesAll of the hypotheses and analyses detailed for Study 1 also apply to Study 2.

We expected in general that the average DG transfer for each condition in Study 2 would be less than

the corresponding DG transfer in Study 1, relative to half-endowment. However, the between-study

comparison of DG transfers was not of intrinsic theoretical interest and thus was not subjected to

systematic statistical analysis. One scenario, however, merits special note. According to proponents of

the religious priming hypothesis, religious primes increase prosociality and thus DG transfers only

when baseline levels of participant generosity are sufficiently low. Results from Gomes & McCullough

[28] can be interpreted to suggest that in some circumstances, baseline generosity levels may be too

high for religious priming to work. If relatively high levels of DG transfers emerged in the neutral

prime conditions of Study 1 (approx. 44% of endowment versus 14–33% of the endowment), null

results might again be attributed to violations of this hypothesized boundary condition. We included

Study 2, with its modified DG paradigm, primarily to address this possibility. Even if Study 1 yielded

baseline levels of generosity deemed too high for religious priming to work, the modified paradigm

of Study 2 was likely—given the results of List [30]—to produce lower baseline transfers, and thus, it

might still enable a test of whether religious priming increases DG transfers under conditions that fall

within boundaries acknowledged as appropriate by proponents of religious priming.


Table 4. Descriptive statistics for DG transfers in Experiment 2.

priming method religiosity

priming condition

mean+ s.d. (N )

control religious priming total

explicit non-religious 20.030+ 0.336

(52)

20.082+ 0.296

(39)

20.052+ 0.319

(91)

religious 20.009+ 0.289

(96)

0.053+ 0.290

(64)

0.016+ 0.290

(160)

total 20.016+ 0.306

(148)

0.002+ 0.298

(103)

20.009+ 0.302

(251)

implicit non-religious 20.089+ 0.321

(92)

20.096+ 0.328

(82)

20.093+ 0.324

(174)

religious 20.007+ 0.317

(191)

20.055+ 0.324

(146)

20.027+ 0.320

(337)

total 20.033+ 0.320

(283)

20.070+ 0.325

(228)

20.050+ 0.322

(511)

total non-religious 20.068+ 0.327

(144)

20.092+ 0.317

(121)

20.079+ 0.322

(265)

religious 20.007+ 0.307

(287)

20.022+ 0.317

(210)

20.013+ 0.311

(497)

total 20.028+ 0.315

(431)

20.047+ 0.318

(331)

20.036+ 0.316

(762)


sci.5:17023820


5.4. ResultsDescriptive statistics for Experiment 2 appear in table 4; means reported in the text are estimated

marginal means. A summary of Study 2 results appears in tables 5 and 6. In the main text, we report

only the results obtained using the pre-registered exclusion criteria, but we note whether conclusions

differ when all subjects are included in the sample (see electronic supplementary material, tables S5

and S6 for a summary of Study 2 results using all subjects). Where indicated, p-values have been

adjusted to reflect one-tailed hypothesis tests. For effect sizes, we report Hedges’ g. We standardized gby using the overall error term from the relevant linear model, and we corrected for bias using the

procedure recommended by Borenstein et al. [42]. We report R2change as the effect size for analyses

testing whether the effect of religious priming is moderated by the extent to which subjects view God

as a punishing, authoritarian figure.

5.5. Did religious subjects transfer more money than did non-religious subjects?Yes. As in Experiment 1, there were significant main effects of subject religiosity: among implicitly

primed subjects, F1,507 ¼ 4.27, p ¼ 0.039 (two-tailed; GLM #2), and among subjects considered

regardless of priming method, F1,754 ¼ 7.49, p ¼ 0.006 (two-tailed; GLM #1). For explicitly primed

subjects, the effect of religiosity was marginally significant, F1,247 ¼ 3.80, p ¼ 0.052 (two-tailed; GLM

#3). Across all subjects, religious subjects transferred an average of seven cents ($0.07) more than did

non-religious subjects, g ¼ 0.22, 95% CI [0.07, 0.37].

5.6. Prediction 1A: Was there a main effect of religious priming, such that the average DictatorGame transfer for all subjects receiving a religious prime (regardless of priming method)exceeded the average Dictator Game transfer for all subjects receiving a neutral prime?

No. Results from GLM #1 indicated that the DG transfers of religiously primed subjects (M ¼ 2$0.045)

did not significantly differ from those of subjects who received a neutral prime (M ¼ 2$0.034),


Tabl

e5.

Expe

rimen

t2re

sults

sum

mar

y:pr

imin

gef

fects

and

inte

ractio

nsw

ithre

ligios

ity.

pred

iction

mod

elm

ean

diffe

renc

es.e

.p- va

lues

an1 pr

ime

n2 cont

rol

gs.e

.of

gBa

yes

unifo

rmBa

yes

norm

alBa

yes

1/2

norm

al

1A:p

rimin

gef

fect,

all

met

hods

and

subj

ects

GLM

#12

0.01

10.

026

0.67

133

143

12

0.04

0.07

0.08

0.05

0.13

1B:i

mpl

icitp

rimin

gef

fect,

all

subj

ects

GLM

#22

0.02

80.

030.

820

228

283

20.

090.

090.

070.

050.

11

1C:e

xplic

itpr

imin

gef

fect,

all

subj

ects

GLM

#30.

005

0.04

0.45

210

314

80.

020.

130.

210.

130.

31

2A:i

nter

actio

nw

ithre

ligios

ity,

allm

etho

ds

GLM

#10.

037

0.05

00.

237

331

431

——

1.05

bn.

a.n.

a.

2A:i

nter

actio

nw

ithre

ligios

ity,

allm

etho

ds

GLM

#10.

037

0.05

00.

237

331

431

——

0.49

cn.

a.n.

a.

2A:p

rimin

gef

fect,

all

met

hods

,reli

giou

ssu

bs

GLM

#10.

007

0.03

10.

410

210

287

0.02

0.09

0.18

0.1

0.28

2A:p

rimin

gef

fect,

all

met

hods

,non

-relig

ious

subs

GLM

#12

0.03

00.

041

0.76

512

114

42

0.10

0.12

0.1

0.07

0.16

2B:i

nter

actio

nw

ithre

ligios

ity,

impl

icitp

rimin

gon

ly

GLM

#22

0.04

0.05

90.

750

228

283

——

0.00

dn.

a.n.

a.

2B:i

nter

actio

nw

ithre

ligios

ity,

impl

icitp

rimin

gon

ly

GLM

#22

0.04

0.05

90.

750

228

283

——

0.18

cn.

a.n.

a.

2B:i

mpl

icitp

rimin

gef

fect,

relig

ious

subs

only

GLM

#22

0.04

80.

035

0.91

214

619

12

0.15

0.11

0.07

0.07

0.12

2B:i

mpl

icitp

rimin

gef

fect,

non-

relig

ious

subs

only

GLM

#22

0.00

70.

049

0.55

882

922

0.02

0.15

0.16

0.1

0.25 (C

ontin

ued.

)


sci.5:17023821



Tabl

e5.

(Con

tinue

d.)

pred

iction

mod

elm

ean

diffe

renc

es.e

.p- va

lues

an1 pr

ime

n2 cont

rol

gs.e

.of

gBa

yes

unifo

rmBa

yes

norm

alBa

yes

1/2

norm

al

2C:i

nter

actio

nw

ithre

ligios

ity,

expl

icitp

rimin

gon

ly

GLM

#30.

114

0.08

0.07

810

314

8—

—1.

62b

n.a.

n.a.

2C:i

nter

actio

nw

ithre

ligios

ity,

expl

icitp

rimin

gon

ly

GLM

#30.

114

0.08

0.07

810

314

8—

—1.

90c

n.a.

n.a.

2C:e

xplic

itpr

imin

gef

fect,

relig

ious

subs

only

GLM

#30.

062

0.04

90.

102

6496

0.21

0.16

0.95

0.95

1.26

2C:e

xplic

itpr

imin

gef

fect,

non-

relig

ious

subs

only

GLM

#32

0.05

20.

064

0.79

339

522

0.17

0.21

0.17

0.12

0.25

a p-va

lues

are

one-

taile

d.b Ba

yes

facto

rrefl

ects

orig

inal

pre-

regi

stere

dan

alytic

alstr

ateg

y.c Ba

yes

facto

rrefl

ects

revis

edan

alytic

alstr

ateg

y.d Ba

yes

facto

rrefl

ects

orig

inal

pre-

regi

stere

dan

alytic

alstr

ateg

y,bu

tthe

relig

ious

prim

ing

effe

ctwa

sin

the

dire

ction

coun

tert

oth

eory

,ren

derin

gco

mpu

tatio

nof

the

Baye

sfac

toru

sing

pre-

regi

stere

dm

etho

dspr

oblem

atic.


sci.5:17023822



Table 6. Experiment 2 results summary: interactions with view of God as authoritarian figure.

prediction modelmeandifference s.e. p-valuesa

Bayes factoruniform


all methods

GLM #4 20.001 0.025 0.518 1.07b


all methods

GLM #4 20.001 0.025 0.518 0.86c



GLM #5 0.004 0.030 0.452 0.00d



GLM #5 0.004 0.030 0.452 1.02c



GLM #6 20.016 0.047 0.634 1.34b



GLM #6 20.016 0.047 0.634 1.17c

ap-values are one-tailed.bBayes factor calculated from High/Low split.cBayes factor calculated from High/Low split; non-pre-registered analytical approach.dBayes factor calculated from High/Low split; the priming effect among subjects with highly authoritarian views of God was inthe direction counter to theory, rendering computation of the Bayes factor using pre-registered methods problematic.


sci.5:17023823


t754 ¼ 20.443, s.e. ¼ 0.026, p ¼ 0.671 (one-tailed), g ¼ 20.04, 95% CI [20.18, 0.11]. Bayesian analyses of

the non-significant finding supported the null hypothesis regardless of how we modelled the predictions

of the priming hypothesis, with Bayes factors ranging from 0.05 (normal distribution) to 0.13 (half-

normal distribution). These findings constitute evidence that religious priming did not increase DG

transfers, considering subjects irrespective of their religiosity.

5.7. Prediction 1B: Was there a simple effect of implicit religious priming, such that the averageDictator Game transfer for all subjects receiving an implicit religious prime exceeded theaverage Dictator Game transfer for all subjects receiving an implicit neutral prime?

No. GLM #2 indicated that the DG transfers of subjects who received an implicit religious prime

(M ¼ 2$0.075) did not differ significantly from those of subjects who received an implicit neutral prime

(M ¼ 2$0.048), t507 ¼ 20.915, s.e.¼ 0.030, p ¼ 0.820 (one-tailed), g ¼ 20.09, 95% CI [20.26, 0.09].

Bayesian analyses of the non-significant finding supported the null hypothesis regardless of how we

modelled the predictions of the priming hypothesis, with Bayes factors ranging from 0.05 (normal

distribution) to 0.11 (half-normal distribution). These findings furnish evidence that implicit religious

priming did not increase DG transfers, considering subjects irrespective of their religiosity.

5.8. Prediction 1C: Was there a simple effect of explicit religious priming, such that the averageDictator Game transfer for all subjects receiving an explicit religious prime exceeded theaverage Dictator Game transfer for all subjects receiving an explicit neutral prime?

No. GLM #3 indicated that the DG transfers of subjects who received an explicit religious prime

(M ¼ 2$0.014) were not significantly greater than those of subjects who received an explicit neutral

prime (M ¼ 2$0.019), t247 ¼ 0.122, s.e. ¼ 0.040, p ¼ 0.452 (one-tailed), g ¼ 0.02, 95% CI [20.23, 0.27].

Bayesian analyses of the non-significant finding supported the null hypothesis regardless of how we

modelled the predictions of the priming hypothesis, with Bayes factors ranging from 0.13 (normal

distribution) to 0.31 (half-normal distribution). These findings constitute evidence that explicit

religious priming did not increase DG transfers, considering subjects without regard to their



sci.5:17023824


religiosity. We note, however, that this conclusion does not hold if all subjects are included in the

analyses. If the full sample (without exclusions) is analysed, the data appear to be insensitive to a

possible effect of explicit priming, based on Bayes factors generated using both the uniform

distribution (0.38) and the half-normal distribution (0.57) to model the predictions of the priming

hypothesis. See electronic supplementary material, table S5.

5.9. Prediction 2A: Was there a significant two-way interaction such that the main effect ofreligious priming (regardless of priming method) was greater for religious subjects thanfor non-religious subjects, and the effect on non-religious subjects did not differstatistically from zero?

No. Results from GLM #1 indicated that the interaction of priming condition with subject religiosity was

not significant, t754 ¼ 0.717, s.e. ¼ 0.051, p ¼ 0.237 (one-tailed). There was no significant effect of religious

priming, either for religious subjects (t754 ¼ 0.23, s.e. ¼ 0.031, p ¼ 0.410 (one-tailed), g ¼ 0.02, 95% CI

[20.16, 0.20]) or for non-religious subjects (t754 ¼ 20.73, s.e. ¼ 0.041, p ¼ 0.765 (one-tailed), g ¼ 20.10,

95% CI [20.33, 0.15]). Regarding this interaction, Bayesian analyses conducted using our revised

methods (see Data analyses) were unable to distinguish the null hypothesis from the priming

hypothesis, with a Bayes factor equal to 0.49, indicating that the data were insensitive to the

interaction effect.

When we examined the simple effects of religious priming, Bayesian analyses provided

evidence that religious priming did not affect the DG transfers of specifically religious subjects,

with Bayes factors ranging from 0.10 (normal distribution) to 0.28 (half-normal distribution).

Bayesian analyses likewise furnished reliable evidence that religious priming had no effect on

specifically non-religious subjects, with Bayes factors ranging from 0.07 (normal distribution) to

0.16 (half-normal distribution).

5.10. Prediction 2B: Among implicitly primed subjects only, was there a significant interaction,such that the simple effect of implicit religious priming was positive and greater forreligious subjects than for non-religious subjects, and that the effect on non-religioussubjects did not differ statistically from zero?

No. The interaction of priming condition with subject religiosity was not significant in GLM #2,

t507 ¼ 20.676, s.e. ¼ 0.059, p ¼ 0.750. There was no significant effect of implicit religious priming,

either for religious subjects (t507 ¼ 21.37, s.e. ¼ 0.035, p ¼ 0.912 (one-tailed), g ¼ 20.15, 95% CI

[20.36, 0.07]) or for non-religious subjects (t507 ¼ 20.14, s.e. ¼ 0.049, p ¼ 0.558 (one-tailed), g ¼ 20.02,

95% CI [20.32, 0.28]).

Our revised analytical methods (see Data analyses) resulted in a Bayes factor of 0.18, supporting the

null hypothesis of no interaction. When we considered the simple effects of implicit religious priming,

Bayesian analyses provided evidence that implicit religious priming did not affect the DG transfers of

specifically religious subjects, with Bayes factors ranging from 0.07 (normal distribution) to 0.12 (half-

normal distribution). Similarly, Bayesian analyses furnished reliable evidence that implicit religious

priming had no effect on specifically non-religious subjects, with Bayes factors ranging from 0.10

(normal distribution) to 0.25 (half-normal distribution). Altogether, these analyses indicate that the

effect of implicit religious priming did not differ based on religiosity of the subjects, and that the

priming effect was zero rather than positive for both non-religious and religious subjects alike.

5.11. Prediction 2C: Among explicitly primed subjects only, was there a significant interaction,such that the simple effect of explicit religious priming was positive and greater forreligious subjects than for non-religious subjects, and that the effect on non-religioussubjects did not differ statistically from zero?

No. In GLM #3, the interaction of priming condition with subject religiosity was marginally significant

using a one-tailed test, t247 ¼ 1.43, s.e. ¼ 0.080, p ¼ 0.078 (one-tailed). There was no significant effect of



sci.5:17023825


explicit religious priming, either for religious subjects (t247 ¼ 1.27, s.e. ¼ 0.049, p ¼ 0.102 (one-tailed), g ¼0.21, 95% CI [20.11, 0.52]) or for non-religious subjects (t247 ¼ 20.81, s.e. ¼ 0.064, p ¼ 0.793 (one-tailed),

g ¼ 20.17, 95% CI [20.59, 0.24]).

For the interaction, Bayesian analyses conducted using our revised methods (see Data analyses) were

unable to distinguish the null hypothesis from the priming hypothesis, yielding a Bayes factor ¼ 1.90. An

analysis of the simple effects of explicit priming did not exclude the possibility that explicit priming of

specifically religious subjects increases DG transfers, with Bayes factors ranging from 0.95 (uniform

distribution) to 1.26 (half-normal distribution). However, Bayesian analyses of the simple effect of

priming on specifically non-religious subjects did argue against the priming hypothesis, with Bayes

factors ranging from 0.12 (normal distribution) to 0.25 (half-normal distribution). We note that this

conclusion is perhaps slightly qualified if the full sample (without exclusions) is analysed. When we

modelled the priming hypothesis using the half-normal distribution and the entire sample, the

resulting Bayes factor (0.36) suggested that the data may be insensitive (see electronic supplementary

material, table S5).

Overall, the Experiment 2 data tend to argue against an effect of explicit priming on non-religious

subjects, but do not exclude an effect of explicit religious priming on religious subjects, or different

effects of explicit religious priming for religious versus non-religious subjects.

5.12. Prediction 3: Did the effect of implicit religious priming remain significant even afterremoving from the analysis any subjects who reported conscious awareness of religiouswords during the suspicion probe?

Because there were no significant effects of implicit religious priming, we did not perform this analysis.

5.13. Prediction 4: Were the effects of religious priming moderated by the extent to whichsubjects viewed God as authoritarian?

There were 341 Experiment 2 subjects who identified as Christian after exclusions for suspicion, essay

length and insufficient attention to the task. This subsample formed the basis of the moderation

analyses that follow. To test for moderation of the religious priming effect, we regressed DG transfers

on priming condition, subject’s authoritarian view of God and the interaction of the two.

GLM #4: Results for all Christian subjects, regardless of priming method. Using all 341 Christian subjects,

we found no significant interaction between authoritarian views of God and priming condition,

t336 ¼ 20.045, s.e. ¼ 0.025, p ¼ 0.518 (one-tailed), R2change ¼ 0:000.

To interpret this non-significant result using Bayesian analyses, we split the sample of Christian

subjects into two groups based on authoritarian views of God, as described previously. After we did

so, 121 subjects remained, with cell sizes ranging from 39 (for neutrally primed subjects with low

authoritarian views of God) to 25 (for religiously primed subjects with low authoritarian views of

God, and for neutrally primed subjects with high authoritarian views of God). There was no

significant interaction between authoritarian views of God and priming condition, t117 ¼ 0.592, s.e. ¼

0.119, p ¼ 0.277 (one-tailed). Bayesian analyses of this non-significant interaction (using our revised

analytical strategy—see Data analyses) returned a Bayes factor of 0.86, indicating that the data were

insensitive rather than being supportive of the null.

GLM #5: Results for implicitly primed Christian subjects. We then restricted the sample to implicitly

primed Christians (N ¼ 234) before testing whether authoritarian views of God moderated the effect

of religious priming on DG transfers. The interaction was not significant, t229 ¼ 0.120, s.e. ¼ 0.030,

p ¼ 0.452 (one-tailed), R2change ¼ 0:000.

To perform Bayesian analyses of this non-significant result, we again split the sample of Christian

subjects into two groups based on authoritarian views of God. After we did so, 86 subjects remained,

with individual cell sizes ranging from 17 (for religiously primed subjects with low authoritarian


subsample to regress DG transfers on priming condition, the grouping variable that captured

authoritarian views of God (High versus Low), and the interaction of the two, we found that the

interaction between authoritarian views of God and priming condition was not significant, t82 ¼ 0.572,

s.e. ¼ 0.142, p ¼ 0.284 (one-tailed). Using our revised analytical strategy (see Data analyses), we

obtained a Bayes factor of 1.02, indicating that the data provided no evidence against the null hypothesis.



sci.26


GLM #6: Results for explicitly primed Christian subjects. Finally, we tested for moderation of the priming

effect after restricting the sample to explicitly primed Christian subjects (N ¼ 107). The regression of

priming condition, authoritarian views of God and the interaction of the two yielded a non-significant

interaction, t103 ¼ 20.342, s.e. ¼ 0.047, p ¼ 0.634 (one-tailed), R2change ¼ 0:001.

To interpret this null result using our pre-registered Bayesian analyses, we split the sample of

Christian subjects into two groups, based on whether they viewed God as high or low in

authoritarianism. This resulted in a subsample of 35 subjects, with individual cell sizes ranging from

4 (for neutrally primed subjects with highly authoritarian views of God) to 14 (for neutrally primed

subjects with low authoritarian views of God). When we regressed DG transfers on priming

condition, the grouping variable that captured authoritarian views of God (High versus Low), and the

interaction of the two, the interaction was not significant, t31 ¼ 0.891, s.e. ¼ 0.212, p ¼ 0.190 (one-

tailed). Bayesian analyses using our revised analytical strategy (see Data analyses) indicated that the

data were insensitive rather than supportive of the null (Bayes factor ¼ 1.17).

Considering all of the moderation analyses for Experiment 2 together, we thus find that the data do

not rule out the possibility that the effect of religious priming among Christian subjects varies as a

function of how authoritarian the subject understands God to be.

5:170238
5.14. DiscussionTo test whether religious priming increases DG transfers, Experiment 2 used a modified DG that gave
dictators the option to take as well as to give money. Apart from this innovation—which was

introduced to encourage high baseline levels of selfish behaviour—Experiment 2 closely paralleled

Experiment 1, making use of the same implicit and explicit primes. We again obtained consistently

non-significant results and used Bayesian analyses to assess whether the data were insensitive or

actively supported the null hypothesis.

When we considered the combined results—irrespective of priming method or subjects’ religiosity—

we found substantial evidence favouring the null hypothesis that religious priming does not increase DG

transfers. In contrast to what we found for Experiment 1, however, the conclusions suggested by our

Bayesian analyses did not vary according to the way we chose to model the predicted distribution of

the priming effect, and the combined data were anywhere from 7.5 to 20 times more likely under the

null than under the priming hypothesis. When we examined implicit and explicit priming separately,

we found evidence that neither method increased DG transfers among subjects considered without

regard to their religiosity. Evidence against an implicit priming effect was very strong: the data were

9–20 times more likely under the null hypothesis (for explicit priming, the data were 3–7.5 times

more likely under the null).

Perhaps, the effects of religious priming were to be found only among religious subjects? For implicit

priming, the Experiment 2 data indicated that this was not the case, providing substantial evidence

against an implicit priming effect in religious subjects and in non-religious subjects, along with

evidence that the effect did not vary by religiosity. For explicit priming, the picture may be more

nuanced. Experiment 2 argued against an effect of explicit priming among non-religious subjects, but

it offered no evidence against the proposition that explicit priming increases DG transfers among

religious subjects, with a small effect size (g ¼ 0.21).

Like Experiment 1, Experiment 2 had the secondary goal of testing whether the effect of religious

priming (among Christian subjects only) depended upon the extent to which subjects conceptualized

God as a punishing, authoritarian figure. Our results, though non-significant, failed to provide any

meaningful evidence in favour of the null hypothesis. Experiment 2 thus does not argue against the

possibility that the effect of religious priming among Christian subjects may vary as a function of how

punishing or authoritarian they understand God to be.

6. Study 3We updated Gomes and McCullough’s meta-analysis [28] with the results of our implicit priming

data, using a random-effects meta-analysis, PET-PEESE estimation, and the trim and fill procedure

employed by Shariff et al. [12]. These meta-analyses included the data from Gomes & McCullough [28],

the data collected in the two experiments performed here, and new experiments by other

investigators of which we became aware. Finally, we conducted a random-effects meta-analysis on



sci.5:17023827


available pre-registered replications (including those presented herein) that used implicit priming, thus

obviating the need for bias-corrected meta-analytical estimates.

Our search for new studies generally followed the approach of Shariff et al. [12], but was restricted

to experiments using the DG as the outcome measure, which greatly circumscribed the number of

relevant studies. First, we searched Google Scholar, PsycINFO and Web of Knowledge for studies

that were published in 2014 or later and that contained the keywords dictator, prim*, god and

relig*. Second, we used Google Scholar to determine which studies published in 2014 or later

cited any of [12,23,28]. Third, we searched the website of the Society of Personality and Social

Psychology for relevant presentations at any of their annual conferences since 2014, using the key

word relig*. Finally, on 18 January 2018, we sent out a call for religious priming experiments on

the SPSP listserv. Our efforts revealed one new experiment that used implicit religious priming in a

Japanese sample [43].

All meta-analyses were conducted using the metafor package in R v. 3.3.2 [44] and reflect the Knapp

and Hartung adjustment for small sample sizes ([45,46]; see also [47,48]). Effect sizes were Hedges’ g. For

studies that appeared in Gomes & McCullough [28], we determined Hedges’ g and the associated

variance from their table 3. For Miyatake & Higuchi [43], we calculated Hedges’ g and the associated

variance from the reported t-value and sample sizes. We then added the results of our own

Experiment 1 and Experiment 2 for implicitly primed subjects only.

6.1. ResultsFigure 1 presents the results of the meta-analysis of all nine studies, which includes both pre-registered

and non-pre-registered experiments and considers participants irrespective of religiosity or other

moderating variables. Without adjusting for possible publication bias, the overall weighted effect size

did not differ statistically from zero, g ¼ 0.21, s.e. ¼ 0.13, p ¼ 0.146 95% CI [20.09, 0.52]. We found

evidence that significant variation in effect size is attributable to between-study differences rather than

sampling error, Q ¼ 43.43, d.f. ¼ 8, p , 0.001, I2 ¼ 88.5%. Egger’s test for funnel plot asymmetry [53]

revealed evidence of publication bias, t ¼ 3.72, d.f. ¼ 7, p ¼ 0.008. Results of the trim and fill

procedure, which tends to undercorrect for publication bias [54], suggested an estimated weighted

effect size of g ¼ 0.09, s.e. ¼ 0.14, p ¼ 0.516, 95% CI [20.19, 0.38]. The PET-PEESE procedure, which

tends to overcorrect for publication bias [54], estimates the effect size g that would be produced in an

idealized experiment with a standard error of zero—and thus of infinite precision—by regressing

effect size on standard error and interpreting the intercept (b0) as the expected effect size under those

idealized conditions. In doing so with our sample, we found that b0 ¼ 20.45, s.e. ¼ 0.14, p ¼ 0.016,

95% CI [20.78, 20.11]. When the resulting intercept is significantly different from zero, as is the case

here, Stanley et al. [55] suggest that a more accurate estimate of effect size is obtained from a

regression of effect size on variance. Performing this regression, we found that b0 ¼ 20.13, s.e. ¼ 0.08,

p ¼ 0.165, 95% CI [20.33, 0.07]. PET-PEESE estimation thus suggests that the effect size of implicit

religious priming on DG transfers does not differ statistically from zero.

As an alternative approach to addressing publication bias, we performed a second meta-analysis

using only pre-registered experiments. Here, as figure 2 shows, the overall weighted effect size was

small and just barely statistically different from zero, albeit in the opposite direction predicted by the

religious prosociality hypothesis, g ¼ 20.07, s.e. ¼ 0.01, p ¼ 0.032, 95% CI [20.12, 20.01]. We found

no variation in effect sizes that was attributable to between-study differences, Q ¼ 0.11, d.f. ¼ 2,

p ¼ 0.948, I2 ¼ 0.00%.

7. General discussionProminent theoretical approaches to understanding religion hold that widespread forms of religious

belief promote prosocial behaviour. Because the claim is causal, proponents of these theories have

sought key empirical support from a growing body of experiments based on the use of religious

priming methods. Consistent with these theories, a recent meta-analysis provided evidence for a small

but reliable effect of religious priming on prosociality [12], although other meta-analytical findings

[19] and a pre-registered replication [28] have led to more circumspect conclusions.

In two pre-registered experiments, supplemented by a small-scale meta-analysis, we sought to shed

light on the lingering issues concerning the religious priming evidence. To do so, we focused on a specific

empirical question: ‘Does religious priming—implicit, explicit, or both—increase DG transfers?’


Shariff & Norenzayan [23] study 1 1.03 [ 0.44, 1.62]0.69 [ 0.12, 1.26]

–0.13 [–0.29, 0.03]0.44 [ 0.18, 0.70]0.60 [ 0.26, 0.94]0.03 [–0.44, 0.50]

–0.06 [–0.25, 0.13]–0.05 [–0.23, 0.13]–0.09 [–0.27, 0.09]

0.21 [–0.09, 0.52]

Shariff & Norenzayan [23] study 2Benjamin et al. [49]Ahmed & Salas [50]Hurst [51]Miyatake & Higuchi [43]Gomes & McCullough [28]Billingsley et al. [52] experiment 1Billingsley et al. [52] experiment 2

RE model

–0.5 0 0.5 1.0observed outcome

1.5 2.0

Figure 1. Random-effects meta-analysis of all implicit religious priming studies with DG transfers as the outcome.

Gomes & McCullough [28] standard prime versus control

Billingsley et al. [52] experiment 1 implicit primes

Billingsley et al. [52] experiment 2 implicit primes

–0.3 0.2

–0.06 [–0.25, 0.13]

–0.05 [–0.23, 0.13]

–0.09 [–0.27, 0.09]

–0.07 [–0.12, –0.01]

0.10

observed outcome

–0.1–0.2

RE model

Figure 2. Random-effects meta-analysis of all pre-registered implicit religious priming studies with DG transfers as the outcome.


sci.5:17023828


Overall, the pattern of results from these two experiments pointed to one clear finding. Namely, both

experiments provided evidence that implicit religious priming does not increase monetary transfers in the

DG. In both experiments, subjects who were implicitly primed with religious concepts did not transfer

more money than did subjects implicitly primed with neutral concepts. Indeed, across both

experiments, visual inspection of the raw means would suggest that, if anything, subjects who

received an implicit religious prime might have transferred less money, not more, relative to subjects

who received a neutral implicit prime. Most crucially, however, we conducted Bayesian analyses,

which permitted us to interpret our non-significant results. For both experiments, these analyses

substantially favoured the hypothesis that implicit religious priming has no effect on DG transfers.

This conclusion held when we analysed the data without excluding any participants.

The possibility that implicit priming has no effect on DG transfers may appear to stand in some

tension with major conclusions highlighted in Shariff et al.’s meta-analysis [12]. For instance, Shariff

et al. [12] analysed 92 studies involving a wide range of outcomes (including dependent variables not

related to prosociality) and found that implicit religious priming had a reliable, small-to-medium effect

(g ¼ 0.39) that did not differ markedly from the mean effect size for explicit religious priming (g ¼0.42). When Shariff et al. restricted their meta-analysis to studies involving prosociality, they reported

that religious priming overall—but not necessarily implicit religious priming specifically—still had a

significant effect, even adjusting for publication bias (g ¼ 0.18) [12]. It is not clear from Shariff et al.’smeta-analysis, however, whether there was a significant effect for the 12 (out of 25) studies of prosocial

behaviour that used implicit priming methods, or whether there was a significant effect for religious

priming studies involving specifically the DG, irrespective of priming method. Gomes & McCullough

[28] partially addressed these gaps in their small-scale meta-analysis of the six available implicit

religious priming studies involving the DG. Gomes and McCullough found no statistically significant

effect of implicit religious priming on DG transfers, although the 95% confidence interval was wide

and contained many positive values. Here, we updated Gomes & McCullough [28] with a new meta-

analysis that included results from nine implicit priming experiments. After adjusting for publication

bias, we found that the estimated effect of implicit priming on DG transfers was not significantly

different from zero. When we meta-analysed only the three pre-registered experiments, the estimated

effect size was negative, with a 95% confidence interval that fell just short of zero.

7.1. Boundary conditions?Although Experiments 1 and 2 provided evidence that implicit priming does not increase DG transfers in

general, proponents of the priming hypothesis might attribute the null results to one or more boundary



sci.5:17023829


conditions hypothesized to circumscribe the effect [29]. For instance, the religious priming effect might

be absent or highly attenuated among individuals who are already motivated to behave prosocially [29].

Several facts, however, speak against this possibility in the present experiments. First, subjects in the

control condition of Experiment 1 transferred only 28.4% of their endowment on average, well within

the 14–33% range that Shariff & Norenzayan [29] speculated as necessary for producing a religious

priming effect on DG transfers by reducing selfish motivation. Second, the DG transfers we observed

in our Experiment 1 control condition are also in accord with the results of a pre-registered online

pilot study that we conducted in September 2015, using 200 unprimed subjects from Amazon’s

Mechanical Turk (see pre-registration at https://osf.io/2ghv6/). Following the same DG instructions

as in Experiment 1 here, subjects transferred 24.1% of their endowments on average, which militates

against the possibility that the baseline prosocial motivation of the Mechanical Turk population is too

high for religious priming to exert an effect [29]. Third, there are the results from the control

conditions of Experiment 2: average transfers across all neutral conditions in Experiment 2 were

negative in sign, indicating that the average subject took money instead of giving money. This

suggests that Experiment 2 subjects were not motivated to be ‘hyperfair’. Altogether, there is little

reason to suspect that our null results in Experiments 1 and 2 are due to unusually high levels of

pre-existing prosocial motivation.

Another boundary condition that could be invoked to explain why we found no overall effect of

implicit priming is that the effect obtains only with religious subjects. Indeed, Shariff et al. [12]

reported that the average effect of religious priming on prosocial behaviour (across 17 studies that

used explicit as well as implicit priming methods) was statistically indistinguishable from zero among

non-religious subjects, after adjusting for publication bias. Experiments 1 and 2, however, showed that

implicit priming does not increase DG transfers among religious subjects (or non-religious ones).

It is also unlikely that our evidence against the efficacy of implicit religious priming on DG

transfers reflects characteristics peculiar to the Mechanical Turk population. Shariff et al.’s meta-

analysis of 92 religious priming studies included numerous experiments drawing upon either

Mechanical Turk (13 studies) or other online platforms for data collection (12 studies) [12]. Shariff

et al. [12] reported no differences in the effect of religious priming as a function of experimental

setting, whether that setting was the laboratory, the field, Mechanical Turk or another online

platform. Indeed, Shariff et al.’s meta-analysis provided little reason for concern that religious

priming has significantly less potent effects on Mechanical Turk or other online platforms, relative

to field or laboratory settings.

7.2. Implicit versus explicit religious primingExperiments 1 and 2 converged in providing evidence against an effect of implicit religious priming. In

important respects, they also converged regarding the possible effects of explicit religious priming.

Breaking down the samples by religiosity helps to clarify the nature of this convergence. If we

consider only religious subjects, Experiments 1 and 2 produced nearly identical estimates of the effect

of explicit religious priming, and with almost identical confidence intervals (g ¼ 0.20 and 0.21,

respectively). The effect size for each experiment, taken individually, did not statistically differ from

zero. When combined, however, the two effect size estimates provide evidence of a small but reliable

effect of explicit priming on religious subjects: a random-effects meta-analysis suggests that the

composite effect size for explicit priming of religious subjects is g ¼ 0.21, s.e. ¼ 0.01, p ¼ 0.016, 95% CI

[0.14, 0.27], which is reasonably consistent with conclusions from Shariff et al. [12], who reported a

meta-analytical effect size of religious priming upon ‘religious/high religiosity’ participants

(regardless of priming method) of g ¼ 0.28, after correcting for publication bias. Though it is

worthwhile to note that the effect size estimate we obtained here was so small that one would need a

sample of 620 religious subjects to achieve 80% statistical power for detecting it (using the methods of

our experiments), the effect appears to be reliably larger than zero.

Although our two experiments converged with regard to an effect of explicit priming on religious

participants, the two experiments diverged concerning the possibility of an explicit priming effect

among non-religious individuals. Experiment 2 offered substantive evidence against the possibility;

Experiment 1 did not. Although the results of Experiment 1 were inconclusive with regard to an

explicit priming effect among non-religious subjects, we suggest that the results of Experiment 2

together with the findings from Shariff et al.’s meta-analysis [12] provide sufficient reason to suspect

that the effect of explicit priming on non-religious individuals is either absent or very weak—perhaps

to the point of practical insignificance.

https://osf.io/2ghv6/

https://osf.io/2ghv6/



sci.5:1702330


7.3. An effect of religiosityOur two experiments thus argue against an effect of implicit religious priming upon DG transfers,

collectively provide evidence for a small effect of explicit priming upon religious individuals and

furnish new grounds to suspect that explicit priming has little or no effect on the transfers of non-

religious individuals. We also note that in both experiments religious individuals transferred more

money on average than did non-religious individuals—8.6 cents more in Experiment 1, and 7.0 cents

more in Experiment 2. This effect of religiosity emerged regardless of priming condition and appears

to add to the body of behavioural research suggesting a positive association of religiosity with

prosociality [1,2]. It is interesting that a persistent effect of religiosity emerged even though the

anonymous recipients of these transfers had no previous relationships with the subjects and were

unable to convey any information about whether they actually had a need for some of the dictator’s

money. As closeness and need are two of the major social–psychological cues influencing people’s

regard for others’ welfare, it is not entirely clear that religious people’s larger transfers in the two

experiments reported here reflect a heightened regard for the welfare of strangers per se. Some

researchers have suggested instead that DG transfers may largely reflect a desire to avoid negative

social evaluation [56–58], which raises the possibility that results such as those we obtained here

reflect instead a greater tendency for religious people to share with others out of a heightened

aversion to negative social evaluation for seeming stingy.
8
7.4. Supernatural punishmentUnfortunately, our experiments shed little light on the possibility that the effect of religious priming upon

DG transfers is stronger to the extent that subjects view God as a punishing, authoritarian figure. In

testing whether the effect of religious priming is moderated by how authoritarian subjects understand

God to be, we consistently obtained non-significant results, but Bayesian analyses showed that the

data offer no warrant for favouring the null hypothesis. The data were insensitive regardless of

whether we examined implicit and explicit priming separately, and regardless of whether we broke

down results by subject religiosity. Our ability to address this research question effectively was

hampered by the small sample sizes available for the Bayesian analyses. Future research involving

larger samples will be needed to investigate this question further.

8. ConclusionWe conclude on a practical note. If, as our research suggests, implicit religious priming has little or no

reliable effect on DG transfers, future researchers might consider turning their attention to explicit and

contextual primes. Future pre-registered experiments with explicit religious primes might help to

clarify the reliability and magnitude of the hypothesized priming effects, illuminate the extent to

which any such effects may be artefacts of experimental demand and test hypotheses concerning the

specific psychological processes by which religious cognition might increase prosocial behaviour. By

attending to such methodological priorities, researchers can help to provide the experimental

psychology of religion with a sound empirical basis from which to initiate and to evaluate theory.

Ethics. All research was approved by the Institutional Review Board of the University of Miami. Subjects provided

informed consent via the computer, by clicking ‘Agree’. No animal research or fieldwork was conducted.

Data accessibility. Data are publicly available through the Open Science Framework at the following link: https://osf.io/

6nqwt/.

Authors’ contributions. M.E.M., J.B. and C.M.G. designed the study. J.B. collected the data. J.B. analysed the data. M.E.M.,

J.B. and C.M.G. interpreted the results. J.B., C.M.G. and M.E.M. wrote and edited the proposal and the final

manuscript. All authors give final approval for publication.

Competing interests. We declare we have no competing interests.

Funding. Financial support was provided by the John Templeton Foundation (grant no. 29165).

Acknowledgements. We thank Azim Shariff and Ara Norenzayan for their thoughtful feedback on prior work, helping to

provide the impetus for this research. We further thank Vassilis Saroglou, Michiel van Elk and Ara Norenzayan for

their extensive feedback and commentary on an earlier version of this proposal, as well as Debra Lieberman,

Carlton Patrick, Daniel Forster, William McAuliffe and Eric Pedersen for valuable suggestions. Thanks as well to

Kristin Rycko and Sarah Betancourt for proof-reading.






sci.5:17023831


Appendix A. Text of priming conditions

.



sci.5:17023832


.



sci.5:17023833


For

.



sci.5:17023834


Appendix B. Comparison of methods and instructions used in Shariff &Norenzayan [23] and in our Experiment 1 (implicit primes)

.



sci.5:17023835


.



sci.5:17023836


.



sci.5:17023837


.



sci.5:17023838


.



sci.5:17023839


.

Appendix C. Experiment 2 DG Instructions

.


40


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rsos.royalsocietypublishing.org
R.Soc.opensci.5:170238

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